{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Starting comments\n",
    "Charles Le Losq, Created 7 April 2015 for Python, Modified 30 Sept. 2016 for Julia\n",
    "\n",
    "This IJulia notebook is aimed to show how you can easily fit a Raman spectrum with Julia , for free and, in my opinion, in an elegant way. Julia presents some advantages over Python (speed, in my opinion more simplicity as it is directly made for scientific computing) and inconvenients (young, so may have breakups, not that much libraries). But it is definitely worth the try. For optimisation I think the JuMP package does a really good job, leaving you lots of choice for your optimisation setup and algorithm.\n",
    "\n",
    "The following fitting procedure phylosophie is totally in contradiction with most existing GUI softwares. It probably is a little bit harder to learn for the newcomer, but you have full control over the procedure.\n",
    "\n",
    "In this example, we will fit the 850-1300 cm$^{-1}$ portion of a Raman spectrum of a lithium tetrasilicate glass Li$_2$Si$_4$O$_9$, the name will be abbreviated LS4 in the following. \n",
    "\n",
    "For further references for fitting Raman spectra of glasses, please see for instance: Virgo et al., 1980, Science 208, p 1371-1373; Mysen et al., 1982, American Mineralogist 67, p 686-695; McMillan, 1984, American Mineralogist 69, p 622-644; Mysen, 1990, American Mineralogist 75, p 120-134; Le Losq et al., 2014, Geochimica et Cosmochimica Acta 126, p 495-517 and Le Losq et al., 2015, Progress in Earth and Planetary Sciences 2:22.\n",
    "\n",
    "We will use the optimization algorithms of Ipopt with JuMP. Please consult http://www.juliaopt.org/ for further details."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing libraries\n",
    "So the first part will be to import a bunch of libraries for doing various things (quite straightforward with Julia)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "using JuMP\n",
    "using PyPlot\n",
    "using Ipopt\n",
    "using Spectra\n",
    "using JLD"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing and looking at the data\n",
    "Let's first have a look at the spectrum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x323881d10>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "PyObject <matplotlib.text.Text object at 0x324540390>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get the spectrum to deconvolute, with skipping header and footer comment lines from the spectrometer\n",
    "data = readdlm(\"./data/LS4.txt\", '\\t')\n",
    "\n",
    "# To skip header and footer lines\n",
    "skip_header = 23\n",
    "skip_footer = 121\n",
    "inputsp = zeros(size(data)[1]-skip_header-skip_footer,2)\n",
    "j = 1\n",
    "for i = skip_header+1:size(data)[1]-skip_footer\n",
    "    inputsp[j,1] = Float64(data[i,1])\n",
    "    inputsp[j,2] = Float64(data[i,2])\n",
    "    j = j + 1\n",
    "end\n",
    "\n",
    "# performing the long correction; not always necessary at frequencies > 500 cm-1, \n",
    "# but this is just for the sack of example in the present case\n",
    "inputsp[:,1], inputsp[:,2],~ = tlcorrection(inputsp,23.0,490.0)\n",
    "\n",
    "# create a new plot for showing the spectrum\n",
    "figure()\n",
    "\n",
    "plot(inputsp[:,1],inputsp[:,2],color=\"black\")\n",
    "\n",
    "xlabel(L\"Raman shift, cm$^{-1}$\", fontsize = 14)\n",
    "ylabel(\"Normalized intensity, a. u.\", fontsize = 14)\n",
    "title(\"Figure 1: the spectrum of interest\",fontsize = 14, fontweight = \"bold\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So we are looking at the 500-1300 cm$^{-1}$ portion of the Raman spectrum of the glass. We see a peak near 800 cm$^{-1}$, and two others near 950 and 1085 cm$^{-1}$. We will be interested in fitting the 870-1300 cm$^{-1}$ portion of this spectrum, which can be assigned to the various symmetric and assymetric stretching vibrations of Si-O bonds in the SiO$_2$ tetrahedra present in the glass network (see the above cited litterature for details).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Baseline Removal\n",
    "\n",
    "First thing we notice in Fig. 1, we have to remove a baseline because this spectrum is shifted from 0 by some \"background\" scattering. This quite typical in Raman spectra of glasses. Several ways exist to do so. We're going to the simplest thing: a polynomial fitting the signal base around 870 and 1300 cm$^{-1}$. Other reasonnable solutions include a linear function, and a constant function. The two latter can be fitted between 1300 and 1350 cm$^{-1}$, but we will need to add another peak around 800 cm$^{-1}$. For now, the example is done with fitting the 870 cm$^{-1}$ portion of spectra, as this usually results in more robust final results.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x326ee1d90>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Base.writemime is deprecated.\n",
      "  likely near /Users/charles/.julia/v0.5/IJulia/src/kernel.jl:31\n",
      "WARNING: Base.writemime is deprecated.\n",
      "  likely near /Users/charles/.julia/v0.5/IJulia/src/kernel.jl:31\n",
      "WARNING: Base.writemime is deprecated.\n",
      "  likely near /Users/charles/.julia/v0.5/IJulia/src/kernel.jl:31\n",
      "WARNING: Base.writemime is deprecated.\n",
      "  likely near /Users/charles/.julia/v0.5/IJulia/src/kernel.jl:31\n",
      "WARNING: Base.writemime is deprecated.\n",
      "  likely near /Users/charles/.julia/v0.5/IJulia/src/kernel.jl:31\n",
      "WARNING: Base.writemime is deprecated.\n",
      "  likely near /Users/charles/.julia/v0.5/IJulia/src/kernel.jl:31\n",
      "WARNING: Base.writemime is deprecated.\n",
      "  likely near /Users/charles/.julia/v0.5/IJulia/src/kernel.jl:31\n",
      "WARNING: Base.writemime is deprecated.\n",
      "  likely near /Users/charles/.julia/v0.5/IJulia/src/kernel.jl:31\n",
      "WARNING: Base.writemime is deprecated.\n",
      "  likely near /Users/charles/.julia/v0.5/IJulia/src/kernel.jl:31\n",
      "WARNING: Base.writemime is deprecated.\n",
      "  likely near /Users/charles/.julia/v0.5/IJulia/src/kernel.jl:31\n",
      "WARNING: Base.writemime is deprecated.\n",
      "  likely near /Users/charles/.julia/v0.5/IJulia/src/kernel.jl:31\n",
      "WARNING: Base.writemime is deprecated.\n",
      "  likely near /Users/charles/.julia/v0.5/IJulia/src/kernel.jl:31\n",
      "WARNING: Base.writemime is deprecated.\n",
      "  likely near /Users/charles/.julia/v0.5/IJulia/src/kernel.jl:31\n",
      "in show at /Users/charles/.julia/v0.5/PyCall/src/PyCall.jl\n",
      "WARNING: Base.writemime is deprecated.\n",
      "  likely near /Users/charles/.julia/v0.5/IJulia/src/kernel.jl:31\n",
      "in show at /Users/charles/.julia/v0.5/PyCall/src/PyCall.jl\n",
      "WARNING: Base.writemime is deprecated.\n",
      "  likely near /Users/charles/.julia/v0.5/IJulia/src/kernel.jl:31\n",
      "in show at /Users/charles/.julia/v0.5/PyCall/src/PyCall.jl\n",
      "WARNING: Base.writemime is deprecated.\n",
      "  likely near /Users/charles/.julia/v0.5/IJulia/src/kernel.jl:31\n",
      "in show at /Users/charles/.julia/v0.5/PyCall/src/PyCall.jl\n",
      "WARNING: Base.writemime is deprecated.\n",
      "  likely near /Users/charles/.julia/v0.5/IJulia/src/kernel.jl:31\n",
      "in show at /Users/charles/.julia/v0.5/PyCall/src/PyCall.jl\n",
      "WARNING: Base.writemime is deprecated.\n",
      "  likely near /Users/charles/.julia/v0.5/IJulia/src/kernel.jl:31\n",
      "in show at /Users/charles/.julia/v0.5/PyCall/src/PyCall.jl\n",
      "WARNING: Base.writemime is deprecated.\n",
      "  likely near /Users/charles/.julia/v0.5/IJulia/src/kernel.jl:31\n",
      "in show at /Users/charles/.julia/v0.5/PyCall/src/PyCall.jl\n",
      "WARNING: Base.writemime is deprecated.\n",
      "  likely near /Users/charles/.julia/v0.5/IJulia/src/kernel.jl:31\n",
      "in show at /Users/charles/.julia/v0.5/PyCall/src/PyCall.jl\n",
      "WARNING: Base.writemime is deprecated.\n",
      "  likely near /Users/charles/.julia/v0.5/IJulia/src/kernel.jl:31\n",
      "in show at /Users/charles/.julia/v0.5/PyCall/src/PyCall.jl\n",
      "WARNING: Base.writemime is deprecated.\n",
      "  likely near /Users/charles/.julia/v0.5/IJulia/src/kernel.jl:31\n",
      "in show at /Users/charles/.julia/v0.5/PyCall/src/PyCall.jl\n",
      "WARNING: Base.writemime is deprecated.\n",
      "  likely near /Users/charles/.julia/v0.5/IJulia/src/kernel.jl:31\n",
      "in show at /Users/charles/.julia/v0.5/PyCall/src/PyCall.jl\n",
      "WARNING: Base.writemime is deprecated.\n",
      "  likely near /Users/charles/.julia/v0.5/IJulia/src/kernel.jl:31\n",
      "in show at /Users/charles/.julia/v0.5/PyCall/src/PyCall.jl\n",
      "WARNING: New baseline version requires the revision of old smoothing p values for splines.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "PyObject <matplotlib.legend.Legend object at 0x327298910>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the regions of interest roi\n",
    "roi = [860.0 870.0; 1300.0 1400.0]\n",
    "\n",
    "\n",
    "y_corr, y_bas, xy_roi = baseline(inputsp[:,1],inputsp[:,2],roi,\"gcvspline\",p=0.5,roi_out = \"yes\") \n",
    "\n",
    "#Creates a plot showing the baseline\n",
    "figure()\n",
    "plot(inputsp[:,1],y_corr[:,1],color=\"black\",label=\"Raw data\")\n",
    "scatter(xy_roi[:,1],xy_roi[:,2],color=\"red\",label=\"ROI\")\n",
    "plot(inputsp[:,1],y_bas[:,1],color=\"blue\",label=\"Baseline\")\n",
    "plot(inputsp[:,1],inputsp[:,2],color=\"green\",label=\"Treated sp.\")\n",
    "\n",
    "title(\"Figure 2: the fit of the background\",fontsize = 18, fontweight = \"bold\")\n",
    "\n",
    "# we set the values of the labels of the x and y axis.\n",
    "xlabel(L\"Raman shift, cm$^{-1}$\",fontsize=18) # The L in front of the string indicates that we use Latex for the maths\n",
    "ylabel(\"Intensity, a. u.\",fontsize=18)\n",
    "\n",
    "# we display the legend at the best location, without a frame\n",
    "legend(loc=\"best\",frameon=false)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will do some manipulation to have the interested portion of spectrum in a single variable. We will assume that the errors have not been drastically affected by the correction process (in some case it can be, but this one is quite straightforward), such that we will use the initial relative errors stored in the \"ese0\" variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done\n"
     ]
    }
   ],
   "source": [
    "index_interest = find(867.0 .< inputsp[:,1] .< 1300.0)\n",
    "\n",
    "interestspectra = y_corr[index_interest,1]\n",
    "ese0 = sqrt(abs(interestspectra[:,1]))/abs(interestspectra[:,1]) # the relative errors after baseline subtraction\n",
    "interestspectra[:,1] = interestspectra[:,1]/trapz(inputsp[index_interest,1],interestspectra[:,1])*1000. # normalise spectra to maximum intensity, easier to handle \n",
    "\n",
    "# First we simplify things by calling x, y and the frequency and intensity of spectra for later use\n",
    "sigma = abs(ese0.*interestspectra[:,1]) #calculate good ese\n",
    "x = inputsp[index_interest,1]\n",
    "y = interestspectra[:,1]\n",
    "println(\"Done\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fitting the spectrum\n",
    "\n",
    "All the fitting will be done using JuMP, and the Ipopt solver. Good thing, you can change the solver as you want. JuMP is just a way to express things (a little bit like lmfit under python, but much more flexible).\n",
    "\n",
    "There was a long speach at this point in the Python version of this notebook, but Julia allows to fit very easily the spectrum. It is quite obvious in the following lines that we create a model, we define the variables containing the peaks amplitudes, frequency and widths (hwhm), and we set them. \n",
    "\n",
    "If more or less peaks are needed, simply change the number of peaks (variable m), and adjust the initial parameters after the setValue function calls.\n",
    "\n",
    "Constraints are possible to implement too, quite easily. Usually Ipopt gives a very good results without needing constraints."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Constructed\n"
     ]
    }
   ],
   "source": [
    "#Change the following parameters to adjust the model\n",
    "amp_guess = [1,1,1,1,1]\n",
    "freq_guess = [950,1050,1090,1140,1190]\n",
    "hwhm_guess = [30,30,30,30,30]\n",
    "\n",
    "# we need to know the number of data points as well as the number of peaks\n",
    "n = size(x,1) # number of data\n",
    "m = 5 #number of peaks, to be modified!\n",
    "\n",
    "# The model for fitting baseline to roi signal\n",
    "mod = Model(solver=IpoptSolver())\n",
    "\n",
    "# we declare three variable arrays that contain the amplitudes, frequencies and width of peaks\n",
    "@variable(mod,g_amplitudes[i=1:m] >= 0.0) # we can add constrain during declaration: the amplitude cannot be negative\n",
    "@variable(mod,850 <= g_frequency[i=1:m] <= 1250)\n",
    "@variable(mod,20.0 <= g_hwhm[i=1:m] <= 50) # and we retrain the width to a reasonnable amount\n",
    "\n",
    "# setting initial values\n",
    "setvalue(g_amplitudes[i=1:m],amp_guess[i])\n",
    "setvalue(g_frequency[i=1:m],freq_guess[i])\n",
    "setvalue(g_hwhm[i=1:m],hwhm_guess[i])\n",
    "\n",
    "# add constraint\n",
    "@NLconstraint(mod, freq_g1[j=1:n], 850 <= g_frequency[1] <= 980)\n",
    "@NLconstraint(mod, freq_g2[j=1:n], 980 <= g_frequency[2] <= 1065)\n",
    "@NLconstraint(mod, freq_g3[j=1:n], 1065 <= g_frequency[3] <= 1110)\n",
    "@NLconstraint(mod, freq_g4[j=1:n], 1110 <= g_frequency[4] <= 1170)\n",
    "@NLconstraint(mod, freq_g5[j=1:n], 1170 <= g_frequency[5] <= 1250)\n",
    "@NLconstraint(mod, hwhm_g5[j=1:n], 20 <= g_hwhm[5] <= 35)\n",
    "\n",
    "# here we write the function of our model that allows the direct calculation\n",
    "@NLexpression(mod,g_mod[j=1:n],sum{g_amplitudes[i]*exp(-log(2) * ((x[j]-g_frequency[i])/g_hwhm[i])^2), i = 1:m})\n",
    "\n",
    "# Then we write the objective function\n",
    "@NLobjective(mod,Min,sum{(g_mod[j] - y[j])^2, j=1:n})\n",
    "println(\"Constructed\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And below, we can launch the fitting procedure:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Solving...\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit http://projects.coin-or.org/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "This is Ipopt version 3.12.4, running with linear solver mumps.\n",
      "NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
      "\n",
      "Number of nonzeros in equality constraint Jacobian...:        0\n",
      "Number of nonzeros in inequality constraint Jacobian.:    12990\n",
      "Number of nonzeros in Lagrangian Hessian.............:      120\n",
      "\n",
      "Total number of variables............................:       15\n",
      "                     variables with only lower bounds:        5\n",
      "                variables with lower and upper bounds:       10\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:    12990\n",
      "        inequality constraints with only lower bounds:        0\n",
      "   inequality constraints with lower and upper bounds:    12990\n",
      "        inequality constraints with only upper bounds:        0\n",
      "\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "   0  1.3731216e+04 0.00e+00 1.01e+02  -1.0 0.00e+00    -  0.00e+00 0.00e+00   0\n",
      "   1  1.6718840e+03 0.00e+00 3.80e+01  -1.0 1.03e+01   0.0 1.52e-01 1.00e+00f  1\n",
      "   2  1.2868849e+02 0.00e+00 3.72e+00  -1.0 3.32e+00  -0.5 8.38e-01 1.00e+00f  1\n",
      "   3  8.6319768e+01 0.00e+00 5.47e+00  -1.0 1.00e+01    -  3.10e-01 1.00e+00f  1\n",
      "   4  3.0455536e+01 0.00e+00 2.38e+00  -1.0 1.03e+01    -  5.42e-01 1.00e+00f  1\n",
      "   5  8.4954626e+00 0.00e+00 4.54e-01  -1.0 2.44e+00    -  9.26e-01 1.00e+00f  1\n",
      "   6  6.3139613e+01 0.00e+00 1.45e+00  -1.0 1.05e+01    -  1.00e+00 1.00e+00f  1\n",
      "   7  1.4785573e+02 0.00e+00 1.25e+00  -1.0 1.83e+01    -  1.00e+00 6.16e-01f  1\n",
      "   8  2.1284398e+02 0.00e+00 9.82e-01  -1.0 1.06e+01    -  6.05e-01 1.00e+00f  1\n",
      "   9  1.8718518e+02 0.00e+00 4.65e-01  -1.7 9.59e+00    -  8.35e-01 5.35e-01f  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "  10  1.4067134e+02 0.00e+00 7.90e-02  -1.7 3.64e+00    -  1.00e+00 1.00e+00f  1\n",
      "  11  5.4371230e+01 0.00e+00 1.00e+00  -2.5 1.27e+01    -  6.33e-01 9.89e-01f  1\n",
      "  12  1.8812192e+01 0.00e+00 3.93e-01  -2.5 5.16e+00    -  1.28e-01 1.00e+00f  1\n",
      "  13  1.5971999e+01 0.00e+00 9.56e-03  -2.5 6.32e-01    -  8.82e-01 1.00e+00f  1\n",
      "  14  5.3814851e+00 0.00e+00 3.75e-01  -3.8 6.79e+00    -  6.85e-02 1.00e+00f  1\n",
      "  15  3.3819225e+00 0.00e+00 1.08e-01  -3.8 2.69e+00    -  6.64e-01 1.00e+00f  1\n",
      "  16  3.3619469e+00 0.00e+00 2.91e-03  -3.8 2.62e-02  -1.0 1.00e+00 1.00e+00f  1\n",
      "  17  3.3510195e+00 0.00e+00 2.95e-03  -3.8 7.98e-02  -1.4 1.00e+00 1.00e+00f  1\n",
      "  18  3.3256569e+00 0.00e+00 2.89e-03  -3.8 2.36e-01  -1.9 1.00e+00 1.00e+00f  1\n",
      "  19  3.2618094e+00 0.00e+00 4.58e-03  -3.8 6.70e-01  -2.4 1.00e+00 1.00e+00f  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "  20  3.1338803e+00 0.00e+00 2.60e-02  -3.8 1.59e+00  -2.9 1.00e+00 1.00e+00f  1\n",
      "  21  3.0466560e+00 0.00e+00 2.93e-02  -3.8 1.60e+00  -3.3 1.00e+00 1.00e+00f  1\n",
      "  22  2.9829146e+00 0.00e+00 1.40e-03  -3.8 2.82e-01  -3.8 1.00e+00 1.00e+00f  1\n",
      "  23  1.8822872e+00 0.00e+00 1.14e-01  -5.7 4.15e+00    -  6.12e-01 1.00e+00f  1\n",
      "  24  1.7062460e+00 0.00e+00 1.03e-02  -5.7 8.78e-01  -4.3 9.87e-01 1.00e+00f  1\n",
      "  25  1.6919899e+00 0.00e+00 5.38e-04  -5.7 1.91e-01  -4.8 1.00e+00 1.00e+00f  1\n",
      "  26  1.6690997e+00 0.00e+00 2.78e-04  -5.7 1.98e-01  -5.2 1.00e+00 1.00e+00f  1\n",
      "  27  1.6199054e+00 0.00e+00 2.29e-03  -5.7 4.56e-01  -5.7 1.00e+00 1.00e+00f  1\n",
      "  28  1.5376126e+00 0.00e+00 1.77e-02  -5.7 8.12e-01  -6.2 1.00e+00 1.00e+00f  1\n",
      "  29  1.4373996e+00 0.00e+00 1.43e-01  -5.7 7.41e+00    -  1.00e+00 2.50e-01f  3\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "  30  1.4229421e+00 0.00e+00 1.54e-01  -5.7 3.20e+03    -  2.27e-03 7.12e-04f  4\n",
      "  31  1.3273619e+00 0.00e+00 2.82e-01  -5.7 3.00e+00    -  1.00e+00 1.00e+00f  1\n",
      "  32  1.2061821e+00 0.00e+00 1.08e-03  -5.7 1.82e-01  -5.8 1.00e+00 1.00e+00f  1\n",
      "  33  1.1901079e+00 0.00e+00 2.58e-01  -5.7 2.06e+01    -  1.00e+00 1.31e-01f  3\n",
      "  34  1.0735217e+00 0.00e+00 5.07e-02  -5.7 2.04e+00    -  1.00e+00 1.00e+00f  1\n",
      "  35  1.0457822e+00 0.00e+00 1.49e-02  -5.7 6.73e-01  -6.3 1.00e+00 1.00e+00f  1\n",
      "  36  9.9861674e-01 0.00e+00 7.89e-02  -5.7 1.55e+00  -6.7 1.00e+00 1.00e+00f  1\n",
      "  37  9.6885378e-01 0.00e+00 1.24e-01  -5.7 5.91e+00  -7.2 1.00e+00 3.98e-01f  2\n",
      "  38  9.1821689e-01 0.00e+00 1.65e-01  -5.7 2.35e+00  -6.8 1.00e+00 1.00e+00f  1\n",
      "  39  8.6319516e-01 0.00e+00 1.04e-02  -5.7 1.05e+00  -6.4 1.00e+00 1.00e+00f  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "  40  8.3057819e-01 0.00e+00 7.05e-02  -5.7 4.49e+01    -  1.00e+00 3.69e-02f  1\n",
      "  41  8.2625273e-01 0.00e+00 6.48e-02  -5.7 2.33e+00    -  1.00e+00 7.93e-02f  1\n",
      "  42  8.1014778e-01 0.00e+00 5.43e-03  -5.7 4.44e-01    -  1.00e+00 1.00e+00f  1\n",
      "  43  8.0967898e-01 0.00e+00 6.08e-05  -5.7 5.07e-02    -  1.00e+00 1.00e+00f  1\n",
      "  44  8.0968352e-01 0.00e+00 2.95e-09  -5.7 7.29e-04    -  1.00e+00 1.00e+00f  1\n",
      "  45  7.8055942e-01 0.00e+00 1.23e-02  -8.6 1.95e+00    -  8.83e-01 7.82e-01f  1\n",
      "  46  7.5555261e-01 0.00e+00 1.96e-02  -8.6 2.08e+00    -  9.55e-01 1.00e+00f  1\n",
      "  47  7.4676399e-01 0.00e+00 5.75e-03  -8.6 1.17e+00    -  1.00e+00 1.00e+00f  1\n",
      "  48  7.4496257e-01 0.00e+00 6.48e-04  -8.6 3.56e-01    -  1.00e+00 1.00e+00f  1\n",
      "  49  7.4500657e-01 0.00e+00 3.01e-07  -8.6 5.21e-03    -  1.00e+00 1.00e+00f  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "  50  7.4500603e-01 0.00e+00 4.73e-11  -8.6 9.62e-05    -  1.00e+00 1.00e+00f  1\n",
      "\n",
      "Number of Iterations....: 50\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   2.3038241097468416e-02    7.4500603115374531e-01\n",
      "Dual infeasibility......:   4.7334507598375092e-11    1.5306938361000702e-09\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   2.5059035740934106e-09    8.1035408401662436e-08\n",
      "Overall NLP error.......:   2.5059035740934106e-09    8.1035408401662436e-08\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 75\n",
      "Number of objective gradient evaluations             = 51\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 75\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 51\n",
      "Number of Lagrangian Hessian evaluations             = 50\n",
      "Total CPU secs in IPOPT (w/o function evaluations)   =      2.674\n",
      "Total CPU secs in NLP function evaluations           =      2.179\n",
      "\n",
      "EXIT: Optimal Solution Found.\n",
      "Solver status: Optimal\n",
      "rmsd: 0.7450060311537453\n"
     ]
    }
   ],
   "source": [
    "# Solve for the control and state\n",
    "println(\"Solving...\")\n",
    "status = solve(mod)\n",
    "\n",
    "# Display results\n",
    "println(\"Solver status: \", status)\n",
    "println(\"rmsd: \", getobjectivevalue(mod))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now extract the parameters and the peaks, and plot the results. They speak for themself, no need to adopt a complicated process with first constraining peak frequency for instance, as it was necessary to do in Python."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Amplitude are [1.62957,3.82799,3.89422,2.39063,0.396066];"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x323f29810>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Frequency are [946.381,1049.61,1084.9,1127.32,1204.37];\n",
      "HWHM are [26.1129,50.0,26.2632,50.0,34.9819];\n"
     ]
    }
   ],
   "source": [
    "# parameter extractions\n",
    "amplitudes = getvalue(g_amplitudes)\n",
    "frequency = getvalue(g_frequency)\n",
    "hwhm = getvalue(g_hwhm)\n",
    "\n",
    "model_peaks, peaks = gaussiennes(amplitudes,frequency,hwhm,x) # we construct the model representation and the individual peaks\n",
    "\n",
    "# Doign a new figure\n",
    "figure()\n",
    "\n",
    "#we plot the results\n",
    "plot(x,y,linewidth=2.0,color=\"black\",label=\"Signal to fit\")\n",
    "plot(x,model_peaks,color=\"red\",label=\"Gaussian Model\")\n",
    "\n",
    "for i = 1:5\n",
    "    plot(x,peaks[:,i],color=[1.-i/5.0,1.-i/5.0,i/5.0],label=\"Peak $(i)\")\n",
    "end\n",
    "\n",
    "title(\"Figure 3: the fit of the spectrum\",fontsize = 18, fontweight = \"bold\")\n",
    "\n",
    "# we set the values of the labels of the x and y axis.\n",
    "xlabel(L\"Raman shift, cm$^{-1}$\",fontsize=18) # The L in front of the string indicates that we use Latex for the maths\n",
    "ylabel(\"Intensity, a. u.\",fontsize=18)\n",
    "\n",
    "# we display the legend at the best location, without a frame\n",
    "legend(loc=\"best\",frameon=false)\n",
    "\n",
    "println(\"\")\n",
    "println(\"Amplitude are $(amplitudes);\")\n",
    "println(\"Frequency are $(frequency);\")\n",
    "println(\"HWHM are $(hwhm);\")\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I discussed in the python version of this example that changing the algorithm can give you different results. This is quite true. The good thing is that JuMP just allows you to implement your model. You can change the solver and algorithm without any further difficulty when declaring the model:\n",
    "    mod = Model(solver=IpoptSolver(print_level=0))\n",
    "Very different solvers are available in Julia, and you can choose looking here: http://www.juliaopt.org/\n",
    "\n",
    "Ipopt seems to be pretty good and should fit the needs of most problems quite well. But there is also NLopt, in which you can choose to use the Nelder-Mead algorithm for instance, or Mosek. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Error estimation with bootstrapping\n",
    "\n",
    "Estimation of the errors affecting each parameter will be done with bootstrapping. We first resample with replacement X times our dataset. Then we fit it X times and we save the X parameter sets generated. With X large enough, this provides a robust statistic for the errors on the parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1, solver status: Optimal\n",
      "Iteration 2, solver status: Optimal\n",
      "Iteration 3, solver status: Optimal\n",
      "Iteration 4, solver status: Optimal\n",
      "Iteration 5, solver status: Optimal\n",
      "Iteration 6, solver status: Optimal\n",
      "Iteration 7, solver status: Optimal\n",
      "Iteration 8, solver status: Optimal\n",
      "Iteration 9, solver status: Optimal\n",
      "Iteration 10, solver status: Optimal\n",
      "Iteration 11, solver status: Optimal\n",
      "Iteration 12, solver status: Optimal\n",
      "Iteration 13, solver status: Optimal\n",
      "Iteration 14, solver status: Optimal\n",
      "Iteration 15, solver status: Optimal\n",
      "Iteration 16, solver status: Optimal\n",
      "Iteration 17, solver status: Optimal\n",
      "Iteration 18, solver status: Optimal\n",
      "Iteration 19, solver status: Optimal\n",
      "Iteration 20, solver status: Optimal\n",
      "Iteration 21, solver status: Optimal\n",
      "Iteration 22, solver status: Optimal\n",
      "Iteration 23, solver status: Optimal\n",
      "Iteration 24, solver status: Optimal\n",
      "Iteration 25, solver status: Optimal\n",
      "Iteration 26, solver status: Optimal\n",
      "Iteration 27, solver status: Optimal\n",
      "Iteration 28, solver status: Optimal\n",
      "Iteration 29, solver status: Optimal\n",
      "Iteration 30, solver status: Optimal\n",
      "Iteration 31, solver status: Optimal\n",
      "Iteration 32, solver status: Optimal\n",
      "Iteration 33, solver status: Optimal\n",
      "Iteration 34, solver status: Optimal\n",
      "Iteration 35, solver status: Optimal\n",
      "Iteration 36, solver status: Optimal\n",
      "Iteration 37, solver status: Optimal\n",
      "Iteration 38, solver status: Optimal\n",
      "Iteration 39, solver status: Optimal\n",
      "Iteration 40, solver status: Optimal\n",
      "Iteration 41, solver status: Optimal\n",
      "Iteration 42, solver status: Optimal\n",
      "Iteration 43, solver status: Optimal\n",
      "Iteration 44, solver status: Optimal\n",
      "Iteration 45, solver status: Optimal\n",
      "Iteration 46, solver status: Optimal\n",
      "Iteration 47, solver status: Optimal\n",
      "Iteration 48, solver status: Optimal\n",
      "Iteration 49, solver status: Optimal\n",
      "Iteration 50, solver status: Optimal\n",
      "Iteration 51, solver status: Optimal\n",
      "Iteration 52, solver status: Optimal\n",
      "Iteration 53, solver status: Optimal\n",
      "Iteration 54, solver status: Optimal\n",
      "Iteration 55, solver status: Optimal\n",
      "Iteration 56, solver status: Optimal\n",
      "Iteration 57, solver status: Optimal\n",
      "Iteration 58, solver status: Optimal\n",
      "Iteration 59, solver status: Optimal\n",
      "Iteration 60, solver status: Optimal\n",
      "Iteration 61, solver status: Optimal\n",
      "Iteration 62, solver status: Optimal\n",
      "Iteration 63, solver status: Optimal\n",
      "Iteration 64, solver status: Optimal\n",
      "Iteration 65, solver status: Optimal\n",
      "Iteration 66, solver status: Optimal\n",
      "Iteration 67, solver status: Optimal\n",
      "Iteration 68, solver status: Optimal\n",
      "Iteration 69, solver status: Optimal\n",
      "Iteration 70, solver status: Optimal\n",
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      "Done...\n"
     ]
    }
   ],
   "source": [
    "nb_boot = 500 # times we do the bootstrap\n",
    "\n",
    "params_boot = Array{Float64}(nb_boot,3,m) # for recording the parameters generated\n",
    "\n",
    "for k = 1:nb_boot # bootstrapping loop\n",
    "    \n",
    "    b_x_f, b_y_f = bootsample(x,y) #resampling data with replacement with Spectra.jl bootsample function\n",
    "    \n",
    "    mod2 = Model(solver=IpoptSolver(print_level=0))# The model for fitting baseline to roi signal\n",
    "    \n",
    "    @variable(mod2,g_amplitudes_b[i=1:m] >= 0.0)\n",
    "    @variable(mod2,850 <= g_frequency_b[i=1:m] <= 1250)\n",
    "    @variable(mod2,20.0 <= g_hwhm_b[i=1:m] <= 50.0)\n",
    "\n",
    "    # setting initial values\n",
    "    setvalue(g_amplitudes_b[i=1:m],amp_guess[i])\n",
    "    setvalue(g_frequency_b[i=1:m],freq_guess[i])\n",
    "    setvalue(g_hwhm_b[i=1:m],hwhm_guess[i])\n",
    "    \n",
    "    # set bounds\n",
    "    setupperbound(g_hwhm_b[3], 35.0 )\n",
    "    setupperbound(g_hwhm_b[5], 45.0 )\n",
    "    setlowerbound(g_frequency_b[5], 1160 )\n",
    "    \n",
    "    @NLexpression(mod2,g_mod_b[j=1:n],sum{g_amplitudes_b[i] *exp(-log(2) * ((b_x_f[j]-g_frequency_b[i])/g_hwhm_b[i])^2), i = 1:m})\n",
    "    @NLobjective(mod2,Min,sum{(g_mod_b[j] - b_y_f[j])^2, j=1:n})\n",
    "    status = solve(mod2)\n",
    "    println(\"Iteration $(k), solver status: \", status) # we print some information\n",
    "    \n",
    "    params_boot[k,1,:] = getvalue(g_amplitudes_b)\n",
    "    params_boot[k,2,:] = getvalue(g_frequency_b)\n",
    "    params_boot[k,3,:] = getvalue(g_hwhm_b)\n",
    "end\n",
    "        \n",
    "\n",
    "println(\"Done...\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When this is done, we can save the params_boot matrix somewhere in the system with JLD, the Julia file format. We could also output it as a .txt file to use it with another program for plotting, for instance. Then we construct an array that looks at how the parameter std changes during the bootstrapping, for instance. When stable, we can assume we did enought bootstrap to get good results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "From bootstrapping, values for peak 1:\n",
      "Amplitude mean [1.62723] and std [0.0170851]\n",
      "Raman shift (cm^{-1}) mean [946.279] and std [0.158308]\n",
      "HWHM (cm^{-1}) mean [26.0026] and std [0.160849]\n",
      "\n",
      "From bootstrapping, values for peak 2:\n",
      "Amplitude mean [3.88146] and std [0.221479]\n",
      "Raman shift (cm^{-1}) mean [1049.46] and std [3.38968]\n",
      "HWHM (cm^{-1}) mean [50.0] and std [6.11308e-7]\n",
      "\n",
      "From bootstrapping, values for peak 3:\n",
      "Amplitude mean [5.05073] and std [0.291199]\n",
      "Raman shift (cm^{-1}) mean [1087.02] and std [0.35604]\n",
      "HWHM (cm^{-1}) mean [28.0432] and std [0.602615]\n",
      "\n",
      "From bootstrapping, values for peak 4:\n",
      "Amplitude mean [1.32683] and std [0.377563]\n",
      "Raman shift (cm^{-1}) mean [1134.79] and std [4.65432]\n",
      "HWHM (cm^{-1}) mean [28.317] and std [3.27326]\n",
      "\n",
      "From bootstrapping, values for peak 5:\n",
      "Amplitude mean [1.17573] and std [0.0855865]\n",
      "Raman shift (cm^{-1}) mean [1173.45] and std [2.91354]\n",
      "HWHM (cm^{-1}) mean [44.9102] and std [0.706244]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "save(\"./data/Raman_fitting_example_results.jld\",\"params_boot\",params_boot) # saving the results with JLD\n",
    "\n",
    "for i = 1:m\n",
    "    println(\"From bootstrapping, values for peak $(i):\")\n",
    "    println(\"Amplitude mean $(mean(params_boot[:,1,i],1)) and std $(std(params_boot[:,1,i],1))\")\n",
    "    println(\"Raman shift (cm^{-1}) mean $(mean(params_boot[:,2,i],1)) and std $(std(params_boot[:,2,i],1))\")\n",
    "    println(\"HWHM (cm^{-1}) mean $(mean(params_boot[:,3,i],1)) and std $(std(params_boot[:,3,i],1))\")\n",
    "    println(\"\")\n",
    "end\n",
    "\n",
    "# bootstrap performance recorder\n",
    "bootrecord = zeros(nb_boot,3,m)\n",
    "for k = 1:nb_boot\n",
    "    for j = 1:3\n",
    "    for i = 1:m\n",
    "        if k == 1\n",
    "            bootrecord[k,j,i] = 0.0\n",
    "        else\n",
    "            bootrecord[k,j,i] = sum(std(params_boot[1:k,j,i],1))\n",
    "        end\n",
    "    end\n",
    "    end\n",
    "end"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now look at the bootstrapping results. param_interest_boot_stat allows you to choose the parameter you want to look at (1 for amplitude, 2 for frequency, 3 for HWHM), and peak_interest_boot_stat the peak you want to look at."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x332924150>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Iw8NDaWlpuuuuu/Jd063c3d2tvqHI6e+D7LXcKiQkRL///rtlfb9+/TRmzJg8+85+ZR0oKRhWAZQSYWFhloCQ9Y9UbGysTbvp06fnua9bg8DFixctD6bI8sknnxSgWltubm5Wd6sbhqElS5aoUqVKOT6cIyAgQBs3bsx3OJ40aZJmzZqV453wy5cv144dO6z+kb/77rttnpTnqoeA3Dp2OKex6NnHY2c/RvYeIHFr8F28eLHV8ApJVtOaZYmMjLT6+eDBg4qPj8/xHISFhenw4cNWs2zkl72v3KdPn67IyEjVrl1bYWFhOnfuXK5XJ++//36b/9TZm0Eir89/69atrX42DEPLly/P9f1fvXpVx48fd+r8P/jgg1YP9vj999/1j3/8w9K3ZD2zRZZbj5mfn58++eQTtWjRQrVq1VJYWJj27NmT73rsufXvg7i4OKuff/zxR8uUczmJjIy0+tyuXLlS5cqVy/XBO6tXr7aawhAoKQjHQCly69ypBw4c0GOPPaZ169bpwIEDGjt2rKZOnZrnV5r169e3WdatWzdt3rxZO3bsUO/evbVp0yaXTwmXfe5ak8mknTt3qlWrVpo3b552796tI0eOaPPmzZo6daq6deumqlWr5niDX24SEhL07LPP6o477tAbb7yhVatW6fDhw9q4caNGjRqlqKgom2ndcruCXZDjkH1u46xz9+677+Y4Fj0uLk5lypSxbJ/TnMehoaFq27atZZ9XrlyxjN80mUxq2bKlVSjL8tRTT6lKlSpWQaZTp0566623tHnzZh09etQyVdjw4cNVq1YtPfroozpx4oTTxyA4ONhm2WuvvaadO3dq3759+vTTT9W5c+dcj7OPj4+efPJJq7pXrFih/v37a8uWLdqzZ48GDx6sH374Idf9hIaGWqaNy9rPG2+8oeeff97yOdm/f7+WL1+ut956S02bNlVERITWrFnj9PvPenpgVl0HDx60/L5cuXJ6+umnbba59ZhduXJFo0aN0r59+yzTpj377LMu+TNav359q+MxY8YMffLJJzp48KDmz59vd+7lWw0aNMhq2FB8fLxatmypGTNmaMeOHTp69Ki2bt2qWbNm6ZlnnlHVqlXt/qcAKBFcMOMFAMO1T8jL2v7WqdwuXLhgVKlSxWp6qKzfZ718fHzynJopMzPTqFGjRq77cXd3t5nuLK+p3OxNE3Wrnj17OjTdXdbLkX3e6pVXXsl1Wr3sy93c3IzmzZsbN27csNmPK56QN2XKFKt9uLu7W02tZk+bNm0cmkZs4cKFNk83y/p59uzZOe5/6dKlVk8xy+t82Dv32T/vbrc8BfFWqampRqVKlXLtz8/Pz/D398/183/48GGjXLly+f7831r7sWPHLNMiOvr+7T2t0lGnT582PDw87J6nvn372t0mPj7e8iTAnGqsVq1ann/WHfkzOmPGDJv93Pr+s/99YG8qN8MwjH/84x/5+rPtzJ8noChw5RhwMaOAN8Tktn3FihX13XffKSAgwOpKZNbX8T4+Pvryyy9ttrt1uICbm5umTZsmT09Pu/vx9/fXZ599ZjNrhDM132rOnDl66aWX5ObmZndqu6xlWa+wsDCH952lfPnyVlexbu3DyPYQkC5duuj777+3ulp7q4Kc06whFVn7aNy4sdXUavZ07tzZso1hGPr111914MABm3aPP/64KlasaDWrhPR/N4LlpFOnTvrmm28UHByc45XH7OfAz88vxzG5jnxGvLy8NHPmzBw/b1mf24CAgFz3VadOHS1YsEBeXl529xMUFGQzK4hk+/mvUaOG1q9fr4YNGzr0/t3d3XOd/SQvVapUUfv27W3Ok2R7I16WkJAQTZkyxfLnxN57/e677yQV/CmX/fr100MPPWTTNquvxx9/3PKUwNzO99tvv63x48erTJkyDv3ZLuwblgFnlbpwvGPHDnXp0kWBgYHy8fHRvffeq48++siqzcGDB9W+fXuVL19egYGB6tOnj1N33gP5detf/gXZR06aNGmiffv26YUXXlBYWJjKli2rkJAQ9e7dWzt37lS9evVstrEXxh599FFt3LhRjz32mAIDA1W2bFnVrFlTgwcP1v79+9W2bVuH3k9+37OHh4fee+897d+/X6+++qqaNm2qwMBAlSlTRj4+PqpZs6Y6dOigt956S9u2bdPatWvz3Oet3nzzTe3evVtvv/22OnfurNq1a8vX11ceHh7y9/dXRESEBgwYoHXr1unrr7/O9WasgpzT3bt3a+/evVbb25vL9ladOnWy6dPenMeenp6KiYmxqbFHjx4qV65crn107NhRv//+uz766CN16tRJoaGh8vb2lqenpypVqqRmzZpp8ODBWrx4sc6cOWN3KE5+jk3nzp31888/q2vXrqpUqZI8PT1VrVo19e7dW9u3b1eHDh2s9pnbfnbt2qVevXqpatWqKlu2rGrUqKFBgwZpz549ds+lvc9/nTp1tH37di1evFjR0dGqU6eOypcvLw8PDwUEBKh+/fqKiYnRjBkzlJCQkOf8yXnJGgKR/VW7dm21atUqx20GDhyon376SR06dFBAQIDVe929e7caNWrk0PHP6zy5ublp+fLlevPNN3X33XfLy8tLFSpUUOvWrTV37lwtXrzY8h+SvPobMWKEfv/9d40ZM0YPPvig5Vx7e3srNDRUbdq00ciRI7Vu3Tq7NxQDJYHJKOhlriK0cuVKdenSRY0aNVKPHj3k6+ur3377TTdv3tTEiRMl/XkTQ8OGDRUQEKChQ4cqOTlZkyZNUo0aNbR169Zc5+EEbgfjx4+3zGJgGIbKlCmjxMRElS9fvrhLAwrdgAED9Pnnn1s+/5UrV2YuXQD5UmqSYnJysp555hk99thj+uqrr3JsN2HCBKWmpmrXrl2WaXeaNGmitm3bavbs2cX+9DCgoCZMmKArV64oKipK9913n2U4wKVLlzRnzhzLk72yhg507dqVYIzbxssvv6ygoCA98cQTioiIsMzYcebMGX3yySdWwdiUx42WAGBPqbly/Nlnn2nw4MGKi4tT3bp1lZKSIm9vb5uvdqpUqaLIyEibcZd33XWXwsLCtHLlyqIsG3C5YcOG6f3335f05xCFihUrKjMz0zI/bPbxiaGhodqyZYtT87MCJdGTTz6pb7/9VtKfw0oCAgJ048YNXbp0SZKsgnFERIQ2bdokX1/f4iwZQClTasYcr1q1Sn5+fjp58qTuuusu+fr6ys/PTy+++KLlkbQJCQk6d+6czSNZJalp06bauXNnUZcNFIqsMX+ZmZk6d+6cVTDO+vWBBx7Q6tWrCca47WR9/tPT03X27FmrYJz1a/v27fXjjz8SjAHkW6kZVnHkyBGlp6fr8ccf13PPPaeJEydq7dq1+uCDD3T58mXNnTvXMq7M3l3FVatW1cWLF5Wenp7rXelASffiiy+qatWqWr9+vY4eParz58/r6tWr8vX1VfXq1dW4cWN169ZN7dq1K+5SAZf75z//qfvuu08bNmzQsWPHdP78eaWlpal8+fK644471LRpU/Xs2VMtW7Ys7lIBlFKlJhxfvXpVqampGjRokKZMmSJJeuKJJ3T9+nVNmzZNb775puUZ7bdO2yP9OZWQ9Odz3AnHKM3q1Kmj119/Xa+//npxlwIUuYYNG6phw4bFXQaA21ipCcfe3t6SZPMkoejoaE2dOlVbtmxReHi4JFmGWWSXlpZmtR97EhMTtWLFCtWsWTPXdgAAACgeqampOnbsmNq1a6egoCCX77/UhOOQkBAdOHBAlStXtlqeNX/lpUuXLMMp7E3bc/r0aVWsWDHXq8YrVqxQTEyMC6sGAABAYYiNjVWvXr1cvt9SE47vv/9+/fTTT4qPj1edOnUsyxMSEiT9GZJDQkIUHBys7du322y/devWPL+Kq1mzpqQ/D3bWVWiUHMOGDbMMqUHJwrkp2Tg/JRfnpuTi3JRccXFxiomJseQ2Vys14bh79+6aOHGiZs6cqcjISMvy6dOnq0yZMmrdurUkKSoqSnPmzFF8fLzlLv1Vq1bp8OHDevXVV3PtI2soRXh4uBo1alQ4bwRO8/f357yUUJybko3zU3Jxbkouzk3JV1hDYEtNOG7YsKH69++vWbNmKT09Xa1bt9aaNWv0v//9TyNHjlSVKlUkSSNHjtSiRYsUGRlpeULe5MmT1aBBA/Xt27d43wQAAABKtFITjiVp6tSpqlGjhmbNmqVvvvlGNWrU0HvvvaeXXnrJ0iY0NFTr1q3T8OHDNWLECHl6eqpz586aPHkys1QAAAAgV6UqHLu7u2v06NEaPXp0ru3Cw8O1fPnyIqoKAAAAt4tS84Q8oGfPnsVdAnLAuSnZOD8lF+em5OLc/HWZDMMwiruIkmLHjh26//779euvvzIIHwAAoAQq7LzGlWMAAADAjHAMAAAAmBGOAQAAADPCMQAAAGBGOAYAAADMCMcAAACAGeEYAAAAMCMcAwAAAGaEYwAAAMCMcAwAAACYEY4BAAAAM8IxAAAAYEY4BgAAAMwIxwAAAIAZ4RgAAAAw8yjuAgAAAEqbEydOKDEx0WpZUFCQwsLCiqkiuArhGAAAIB9OnDihevXClZaWYrXcy6ucDh2KIyCXcgyrAAAAyIfExERzMI6V9Kv5Fau0tBSbq8kofbhyDAAA4JRwSY2Kuwi4GFeOAQAAADPCMQAAAGBGOAYAAADMCMcAAACAGTfkAQAAFAPmSi6ZCMcAAABFjLmSSy6GVQAAABQx5kouubhyDAAAUGyYK7mk4coxAAAAYEY4BgAAAMwIxwAAAIAZ4RgAAAAw44Y8AAAAF4mLi7NZxtzFpQvhGAAAoMBOS3JTTEyMzRrmLi5dGFYBAABQYEmSbsp63mLmLi6NuHIMAADgMsxbXNpx5RgAAAAwIxwDAAAAZoRjAAAAwIxwDAAAAJgRjgEAAAAzwjEAAABgRjgGAAAAzAjHAAAAgBnhGAAAADAjHAMAAABmhGMAAADAjHAMAAAAmBGOAQAAADPCMQAAAGBGOAYAAADMCMcAAACAGeEYAAAAMCMcAwAAAGaEYwAAAMCMcAwAAACYEY4BAAAAM8IxAAAAYEY4BgAAAMwIxwAAAIAZ4RgAAAAwIxwDAAAAZoRjAAAAwIxwDAAAAJgRjgEAAACzUhOO161bJzc3N5uXu7u7tm7datX24MGDat++vcqXL6/AwED16dNHiYmJxVQ5AAAASguP4i4gv1555RU1btzYalnt2rUtv4+Pj1erVq0UEBCgiRMnKjk5WZMmTdK+ffu0detWeXiUurcMAACAIlLqkuKDDz6op556Ksf1EyZMUGpqqnbt2qVq1apJkpo0aaK2bdtq9uzZGjBgQFGVCgAAgFKm1AyryO7q1avKzMy0u27x4sXq3LmzJRhLUps2bVS3bl0tXLiwqEoEAABAKVTqwnG/fv3k5+cnLy8vPfzww/r1118t6xISEnTu3DmbYReS1LRpU+3cubMoSwUAAEApU2qGVXh6eqpr167q2LGjgoKCdODAAU2ePFl/+9vftHnzZjVo0ECnT5+WJFWtWtVm+6pVq+rixYtKT09XmTJlirp8AAAAlAKlJhw3b95czZs3t/zcuXNnRUVFqX79+hoxYoS+//57paamSpLKli1rs72Xl5ckKTU1lXAMAAAAu0rdsIrs7rzzTj3++ONas2aNDMOQt7e3JOn69es2bdPS0iTJ0gYAAAC4Vam5cpyT6tWr68aNG7p27ZplOEXW8IrsTp8+rYoVKzp01XjYsGHy9/e3WtazZ0/17NnTNUUDAAAgT/Pnz9f8+fOtll2+fLlQ+yz14fi3336Tl5eXfH195evrq+DgYG3fvt2m3datW9WwYUOH9jllyhQ1atTI1aUCAAAgH+xdnNyxY4fuv//+Quuz1AyrsPeEu927d2vJkiVq166dZVlUVJSWLl2q+Ph4y7JVq1bp8OHD6t69e5HUCgAAgNKp1Fw57tGjh7y9vdWiRQtVqlRJ+/fv1/Tp0+Xr66u3337b0m7kyJFatGiRIiMjNXToUCUnJ2vy5Mkmy2wxAAAgAElEQVRq0KCB+vbtW3xvAAAAACVeqQnHTz75pObOnaspU6boypUrCg4OVteuXTVmzBjVqlXL0i40NFTr1q3T8OHDNWLECHl6eqpz586aPHkys1QAAAAgV6UmHA8ZMkRDhgxxqG14eLiWL19eyBUBAADgdlNqxhwDAAAAhY1wDAAAAJgRjgEAAAAzwjEAAABgRjgGAAAAzAjHAAAAgBnhGAAAADAjHAMAAABmhGMAAADArNQ8IQ8AAKC0iouLy/VnlByEYwAAgEJzWpKbYmJiirsQOIhwDAAAUGiSJN2UFCspPNvy7yWNLpaKkDvCMQAAQKELl9Qo288MqyipuCEPAAAAMCMcAwAAAGaEYwAAAMCMcAwAAACYEY4BAAAAM8IxAAAAYEY4BgAAAMwIxwAAAIAZ4RgAAAAwIxwDAAAAZoRjAAAAwIxwDAAAAJgRjgEAAAAzwjEAAABgRjgGAAAAzAjHAAAAgFm+w/GpU6c0adIkDR06VB988IGSkpIs6wzD0LRp09S4cWNVqFBB1atXV58+fXTo0CGXFg0AAAAUBo/8NF66dKl69uyplJQUy7J//etfWrNmjcLDwzVw4EDNmDFDhmFIkq5cuaK5c+fqf//7n5YtW6bIyEiXFg8AAAC4ksNXjhMSEhQdHa1r167JMAz5+vrKMAydO3dOPXv21Pr16zV9+nSVK1dO0dHReu211xQVFSVPT0+lpqaqV69eSk5OLsz3AgAAABSIw+H4k08+0dWrV1WrVi3t379fly9f1s6dOxUaGqq9e/dq2LBhCgkJ0Z49exQbG6t33nlHX331lbZt26bAwECdOXNGc+bMKcz3AgAAABSIw+F45cqVMplMmjRpksLDwyVJDRo00Lhx42QYhnbt2qU333xTd9xxh9V2ERERGjt2rAzD0LJly1xbPQAAAOBCDofjI0eOSJI6duxotbxt27aW3z/55JN2t+3WrZskae/evfkuEAAAACgqDofja9euyd/fX2XLlrVaXqVKFUmSn5+fAgIC7G5bqVIllS9fXhcuXChAqQAAAEDhcjgcV6hQQampqTbLPTz+nPDC29s71+09PT3zWRoAAABQtBwOx8HBwbpx44bVvMZZatWqZTPWODvDMHTlyhUFBwc7VyUAAABQBBwOx1k34dkbN3z06FFt2rQpx22PHDmi9PR0hYaGOlEiAAAAUDQcDseNGzeWYRjauHFjvjv5/vvvZTKZ1KJFi3xvCwAAABQVh5+Q99RTT8lkMumee+7JVweZmZn65JNPZBgGT8gDAABAieZwOK5bt67+/ve/57sDd3d3xcXFWX4PAAAAlFQOh+OCIBQDAACgNHB4zDEAAABwuyMcAwAAAGaEYwAAAMCMcAwAAACYEY4BAAAAM8IxAAAAYEY4BgAAAMwIxwAAAICZ0+F4+/bt+d5m4sSJznYHAAAAFDqnw3GnTp3022+/Odx+0qRJeuONN5ztDgAAACh0Tofj8+fPq127djp79myebd977z39/e9/d7YrAAAAoEg4HY47duyo33//XR07dtTVq1dzbPfRRx9p+PDhMplMmjx5srPdAQAAAIXO6XD81Vdf6YEHHtDOnTv15JNPKj093abNp59+qpdfflnSn+ONhw0b5nylAAAAQCFzOhx7e3tr2bJlqlu3rlavXq0+ffpYrZ82bZqGDBkiSZowYYJee+21glUKAAAAFLICTeVWsWJFrVixQlWqVNHChQv1yiuvSJJmzpypQYMGyTAMjRs3TiNGjHBJsQAAAEBh8ijoDmrUqKHly5frb3/7mz788EMdP35cS5YskWEYGjNmjEaPHu2KOgEAAIBC55KHgNSvX1/ffPONPD099d133+nmzZsaMWKExo4d64rdAwAAAEXCoSvHCQkJebapW7eu5aa73r17a/DgwXa3CwkJyX+VAAAAQBFwKBxXr17d4R2aTCbFxsYqNjbW7rqMjAzHqwMAAACKkEPh2DAMl3Tmqv0AAAAAhcGhcHzkyJHCrgMAAAAodg6F4zvvvLOw6wAAAACKnUtmqwAAAABuB6U6HE+YMEFubm6qX7++zbqDBw+qffv2Kl++vAIDA9WnTx8lJiYWQ5UAAAAoLQr8EBBJysjI0J49e3Tq1Cldu3Yt1xvvoqOjXdGl4uPj9fbbb8vX19fuulatWikgIEATJ05UcnKyJk2apH379mnr1q3y8HDJ2wYAAMBtpkAp8caNGxozZow+++wzJScn59neZDK5LBy/+uqrat68uTIyMnThwgWrdRMmTFBqaqp27dqlatWqSZKaNGmitm3bavbs2RowYIBLagAAAMDtxelhFRkZGWrfvr0mTZqkK1euKCAgQIZhyGQyqXLlyipTpowMw5BhGCpXrpxCQkJUtWpVlxS9fv16LV68WO+9957d9YsXL1bnzp0twViS2rRpo7p162rhwoUuqQEAAAC3H6fD8axZs7R27VpVqVJFP//8s2U8b6VKlZSQkKCrV6/qxx9/1AMPPCDDMDR58mSdPHmywAXfvHlTL7/8sp577jndc889NusTEhJ07tw5NW7c2GZd06ZNtXPnzgLXAAAAgNuT0+F43rx5MplM+te//qWmTZvarPfw8FCbNm20YcMGPfDAA3rmmWdcEkw//fRTnThxQm+99Zbd9adPn5Yku1epq1atqosXLyo9Pb3AdQAAAOD243Q43rt3rySpa9euVsszMzOtfvbw8NCUKVN048YNTZ482dnuJEkXL17UP//5T40ZM0YVK1a02yY1NVWSVLZsWZt1Xl5eVm0AAACA7JwOx8nJyfLz85OPj49lmaenp65evWrTtn79+vL19dX69eud7U6S9MYbbygwMFBDhgzJsY23t7ck6fr16zbr0tLSrNoAAAAA2Tk9W0VwcLBNEA4MDNSZM2d0/vx5BQcHW5YbhqH09HSdP3/e6UKPHj2q6dOn6/3331d8fLxlv2lpaUpPT9fx48fl5+dnGU6RNbwiu9OnT6tixYoqU6ZMrn0NGzZM/v7+Vst69uypnj17Ol0/AAAA8mf+/PmaP3++1bLLly8Xap9Oh+PQ0FBt27ZNSUlJqlChgiQpIiJCZ86c0YoVKxQTE2Npu379el2/ft0qMOdXfHy8DMPQyy+/rJdeeslmfa1atTR06FC9++67Cg4O1vbt223abN26VQ0bNsyzrylTpqhRo0ZO1woAAICCs3dxcseOHbr//vsLrU+nw3GTJk20bds2/fzzz2rfvr0k6fHHH9ePP/6o//f//p98fHzUsGFD7d69W6+88opMJpMeeughpwuNiIjQ119/bbP8jTfe0NWrV/XBBx+oVq1akqSoqCjNmTNH8fHxluncVq1apcOHD+vVV191ugYAAADc3pwOx0888YQ+/vhjzZ8/3xKOBwwYoI8//lhxcXFWN+oZhiEfHx+NGTPG6UIDAwPVpUsXm+VTpkyRyWTSY489Zlk2cuRILVq0SJGRkRo6dKiSk5M1efJkNWjQQH379nW6BgAAANzenL4h7+GHH9aRI0esplTz9PTU6tWr1a1bN6uHgDRr1kyrV6/W3Xff7ZKib2Uymax+Dg0N1bp161S7dm2NGDFCkydPVufOnbVy5co8xxsDAADgr8vpK8cmk0l33nmnzfLKlStrwYIFun79us6dOyc/Pz+bm9tcac2aNXaXh4eHa/ny5YXWLwAAAG4/TofjvJQtW1bVq1cvrN0DAAAALuf0sAoAAADgdlPgcHz69Gm9/vrratCggSpUqGDzZLpLly7p3//+tyZNmmTz9DwAAACgJCnQsIrVq1era9euunz5sgzDkGR7c1xAQIC++uor7dixQ+Hh4ercuXNBugQAAAAKjdNXjk+dOqWoqCglJSWpY8eO+vLLLxUQEGC3bf/+/WUYhpYtW+Z0oQAAAEBhczoc/+c//9Hly5cVFRWlJUuWqHv37vL09LTbtl27dpL+fEIdAAAAUFI5HY5XrFghk8mk8ePH59m2Vq1aKlu2rH7//XdnuwMAAAAKndPh+Pjx4/Ly8lK9evUcau/r66tr16452x0AAABQ6JwOx25ubpab8PKSmZmpK1euqHz58s52BwAAABQ6p8NxWFiYrl+/rpMnT+bZdsOGDUpPT1edOnWc7Q4AAAAodE6H40ceeUSSNG3atFzbZWRkaNSoUTKZTOrQoYOz3QEAAACFzulwPGzYMJUpU0aTJk3SF198YbfNnj171K5dO23evFm+vr4aPHiw04UCAAAAhc3pcFyzZk1NnTpV6enp6t+/v0JCQpSUlCRJ+tvf/qYaNWrovvvu05o1a+Tu7q7Zs2crKCjIZYUDAAAArlagx0c/88wzWrp0qWrWrKkzZ87o+vXrMgxDGzdu1MmTJ2UYhu644w59//33evLJJ11VMwAAAFAoCvT4aEnq0KGDjhw5ojVr1mjTpk1KSEhQZmamqlSpopYtW+qRRx6Rh0eBuwEAAAAKnUtSq5ubm9q0aaM2bdq4YncAAABAsSjQsAoAAADgduKSK8cZGRlatWqVtm/frnPnzkmSKlWqpMaNG+vhhx9WmTJlXNENAADAbS8uLs5mWVBQkMLCwoqhmr+eAofjzz77TGPHjtX58+ftrg8KCtLYsWM1aNCggnYFAABwGzstyU0xMTE2a7y8yunQoTgCchEoUDgeOHCgpk+fbnmMdJUqVVStWjVJUnx8vM6cOaPz589ryJAh2rFjh6ZPn17wigEAAG5LSZJuSoqVFJ5teZzS0mKUmJhIOC4CTo85XrBggaZNmybDMPT000/rwIEDSkhI0LZt27Rt2zYlJCQoLi5O0dHRMgxDn3/+uRYsWODK2gEAAG5D4ZIaZXuF594cLuV0OP74449lMpn04osvat68ebrrrrts2tSrV0+xsbF68cUXZRiGPv744wIVCwAAABQmp8Px7t27ZTKZNHbs2Dzbjh07ViaTSbt373a2OwAAAKDQFWgqtwoVKjj0SOigoCBVqFChIF0BAAAAhc7pcFy3bl1duXJF165dy7PttWvXdOXKFdWrV8/Z7gAAAIBC53Q47tevnzIyMhwaR/zxxx8rMzNT/fr1c7Y7AAAAoNA5PZXboEGDtGbNGr3xxhtKT0/X8OHD5e3tbdUmLS1N//nPfzR27Fh17dqVuY4BAABQojkdjl944QVVqFBBvr6+GjNmjN555x01bdrUap7jrVu36tq1a/L395e/v7+ef/55m/2YTCZNnTrV+XcAAAAAuIjT4XjGjBkymUyWB4BcvXpVq1evtts2KSlJn3/+uQzDkMlksizP+plwDAAAgJLA6XAcHR1tFXQBAACA0s7pcBwbG+vKOgAAAIBiV6B5jgEAAIDbCeEYAAAAMHN6WAUAAMDt7sSJE0pMTLRaFhcXV0zVoCgQjgEAAOw4ceKE6tULV1paSnGXgiJEOAYAALAjMTHRHIxjJYVnW/O9pNHFUxQKHeEYAAAgV+GSGmX7mWEVtzNuyAMAAADMCMcAAACAGeEYAAAAMCMcAwAAAGaEYwAAAMCsSGareP755yVJzZo1U9++feXmRiYHAABAyVMkKXXGjBmaOXOmnnvuOd1zzz1auHBhUXQLAAAA5EuRXcI1DEOGYejQoUPq2bOnGjVqlPdGAAAAQBEqkmEV6enpkqT4+HitWbNGq1at0tq1a4uiawAAAMBhRRKO3d3dJUlhYWF65pln9MwzzxRFtwAAAEC+cGccAAAAYEY4BgAAAMyKZFgFAAAACiYuLs5mWVBQkMLCwoqhmttXvsNxRkaGFi9erG3btikzM1P33nuvoqKi5Ofnl+t2PXr0UFJSklasWOF0sQAAAH89pyW5KSYmxmaNl1c5HToUR0B2oXyF4z/++EOdOnXSoUOHrJa//vrrmjx5cq432q1fv17nzp1zrkoAAIC/rCRJNyXFSgrPtjxOaWkxSkxMJBy7kMNjjtPS0tShQwcdOnRIhmHI29tb5cuXl2EYunDhgvr376/nn39ehmEUZr0AAAB/UeGSGmV7hefeHE5xOBzPnDlThw8flre3t/773//q8uXLSkpK0q+//qpHHnlEhmFo5syZiomJ0c2bNwuzZgAAAKBQOByOFy9eLJPJpDFjxqhXr16WuYvvu+8+rVy5UuPHj5ckffnll4qOjiYgAwAAoNRxOBzv3btXkvTCCy/YXT9y5EjFxsbK3d1dX331lXr27ElABgAAQKnicDhOSkqSv7+//P39c2zTs2dPzZs3T+7u7lq0aBEBGQAAAKWKw7NV+Pj46Nq1a3m269q1qyQpOjpaixYtkpubm+bOnet8hQAAAEARcTgc165dWzt27NDu3bvVoEGDXNtmD8gLFy6UJGVmZhagTAAAAKDwOTys4oEHHpAkLV261KH2Xbt21dy5c+Xu7q6FCxcqMTHRuQoBAACAIuJwOG7Xrp0Mw9Dnn3/u8Djibt26WQIyAAAAUNI5HI4fffRRtW7dWiEhIdqyZYvDHXTr1k3z589XaGioqlWr5lSRAAAAQFFweMxx2bJltWbNGqc6iYqKUlRUlFPbAgAAAEXF4SvHAAAAwO2OcAwAAACYEY4BAAAAs1ITjg8cOKDu3bvrzjvvlI+Pj4KDg9W6dWu7U8sdPHhQ7du3V/ny5RUYGKg+ffowlRwAAADy5PANecXt+PHjunr1qvr27auQkBClpKTof//7n7p06aJp06ZpwIABkqT4+Hi1atVKAQEBmjhxopKTkzVp0iTt27dPW7dulYdHqXnLAAAAKGKlJil26NBBHTp0sFo2ZMgQNWrUSO+++64lHE+YMEGpqanatWuXZeq4Jk2aqG3btpo9e7alHQAAAHCrUjOswh6TyaTq1asrKSnJsmzx4sXq3Lmz1ZzKbdq0Ud26dS2PsgYAAADsKTVXjrOkpKQoNTVVly9f1rfffqvly5erZ8+ekqSEhASdO3dOjRs3ttmuadOmWr58eVGXCwAAgFLE6XCckJAgSQoMDFTZsmVdVlBeXn31VU2dOlWS5ObmpqioKH344YeSpNOnT0uSqlatarNd1apVdfHiRaWnp6tMmTJFVi8AAABKD6fDcWhoqNzc3HT8+PEifSz0sGHD1K1bNyUkJGjhwoXKzMzU9evXJUmpqamSZDese3l5WdoQjgEAAGCP0+HYx8dHZcqUKdJgLEl169ZV3bp1JUkxMTFq166dunTpop9//lne3t6SZAnL2aWlpUmSpQ0AAPhrOnHihM0Ur0FBQQoLCyumilCSOB2Oa9asqaNHj+rmzZtycyu++/q6du2qgQMH6siRI5bhFFnDK7I7ffq0Klas6NBV42HDhsnf399qWc+ePS1jmwEAQOl04sQJ1asXrrS0FKvlXl7ldOhQHAG5hJk/f77mz59vtezy5cuF2qfT4fjxxx/X22+/re+//16dO3d2ZU35kjWU4vLly6pTp46Cg4O1fft2m3Zbt25Vw4YNHdrnlClT1KhRI5fWCQAAil9iYqI5GMdKCjcvjVNaWowSExMJxyWMvYuTO3bs0P33319ofTp9yfcf//iHatWqpUGDBmn//v2urMmu8+fP2yzLyMjQF198IW9vb919992SpKioKC1dulTx8fGWdqtWrdLhw4fVvXv3Qq8TAACUBuGSGplf4Xm0xV+J01eOv/vuOw0ePFhjxoxRo0aN1KlTJ7Vs2VKVKlWSu7t7jttFR0c71d8LL7ygK1eu6G9/+5uqVaumM2fOaO7cuTp06JDeffddlStXTpI0cuRILVq0SJGRkRo6dKiSk5M1efJkNWjQQH379nWqbwAAAPw1OB2OY2JiZDKZJEmGYejbb7/Vt99+m+s2JpPJ6XD89NNPa+bMmfrss8904cIFlS9fXvfff78mTZqkTp06WdqFhoZq3bp1Gj58uEaMGCFPT0917txZkydPZpYKAAAA5MrpcBwSEmIJx0Whe/fuDg+LCA8P54EfAAAAyDenw/GpU6dcWQcAAABQ7IpvDjYAAACghCEcAwAAAGZOD6vI7tKlS1q7dq2OHz+ulJQUjRw50hW7BQAAAIpUgcJxZmamRo4cqQ8//NDqkc3Zw/GlS5dUp04dpaWlaf/+/apRo0ZBugQAAAAKTYGGVfTo0UOTJ09WWlqa6tWrJw8P26wdEBCgbt26KSUlRQsXLixIdwAAAEChcjocL1iwQIsXL1ZwcLC2bt2qAwcOqGLFinbb9ujRQ5K0Zs0aZ7sDAAAACp3T4XjWrFkymUz697//rcaNG+fatmnTpjKZTEXymGkAAADAWU6H4x07dkiSunbtmmfbcuXKyc/PT+fPn3e2OwAAAKDQOX1D3uXLl+Xv769y5co51N4wDGe7AgAAKHRxcXG5/oy/BqfDcUBAgM6fP6+0tDR5eXnl2vbMmTO6cuWKwsLCnO0OAACgkJyW5KaYmJjiLgQlgNPDKu677z5J0rp16/JsO2vWLElS8+bNne0OAACgkCRJuikpVtKv2V5vFWdRKCZOh+Po6GgZhqHRo0crJSUlx3Y//vij3nzzTZlMJvXp08fZ7gAAAApZuKRG2V53FG85KBZOD6uIiYnR1KlTtXnzZjVv3lyDBg1Senq6pD+nbDt27JiWLFmiJUuWKDMzUx07dlSHDh1cVjgAAADgak6HY5PJpG+//VZPPPGENm3apMGDB1vWPfLII5bfG4ahhx56SPPmzStYpQAAAEAhK9AT8gIDA7V27VrNnDlTzZs3l4eHhwzDkGEYMplMatKkiWbMmKGVK1fKz8/PVTUDAAAAhcLpK8dZ3N3d1a9fP/Xr108ZGRlKTExUZmamgoOD5enp6YoaAQAAgCJR4HBstTMPD1WpUsWVuwQAAACKjNPDKurWrasHH3zQ4fYPPfSQ6tWr52x3AAAAQKFz+srx0aNHdfXqVYfbHzt2TCdOnHC2OwAAAKDQFeiGvPzIzMyUyWQqqu4AAACAfCuScJyWlqazZ88yYwUAAABKNIeHVZw6dcpmWER6erq2bNkiwzDsbmMYhpKSkhQbG6v09HRFREQUrFoAAACgEDkcjmfOnKk333zTatnFixcduikva97j559/Pv8VAgAAAEUkXzfkZb9CbDKZcrxinJ2Pj48iIiI0cOBAxcTE5L9CAAAAoIg4HI7HjBmjUaNGSfozJHt6eqpKlSo6efJkjtu4ublxEx4AAABKDYfDsclkkru7u+Xn6OhoBQQEWC0DAAAASjOn5zmOjY11ZR0AAABAsXPJ46MNw9CuXbt0/PhxpaSkKDo62hW7BQAAAIpUgec5/vTTT1WtWjU1btxYUVFR6t27t9X6S5cuqUGDBoqIiNC5c+cK2h0AAABQaAoUjocOHaohQ4bozJkzKleunN2b7wICAnTvvfcqLi5OixYtKkh3AAAAQKFyOhyvXLlSH374oXx8fPTVV1/pypUrCg4Otts2OjpahmHoxx9/dLpQAAAAoLA5Peb4008/lclk0rhx4xQVFZVr25YtW0qS9u7d62x3AAAAQKFz+srxzz//LEl69tln82zr7++v8uXL6/Tp0852BwAAABQ6p8PxxYsX5efnJz8/P4fau7u7KzMz09nuAAAAgELndDj28/NTcnKy0tPT82x76dIlXb58WUFBQc52BwAAABQ6p8NxRESEDMPQtm3b8mz75ZdfyjAMNW7c2NnuAAAAgELndDju2rWrDMPQ2LFjdfPmzRzb7d27V2+88YZMJpOefvppZ7sDAAAACp3T4fi5557TXXfdpVWrVql9+/b64YcfLCH5jz/+0Jo1azR8+HA1b95cSUlJatKkiXr06OGywgEAAABXc3oqN09PTy1btkzt2rXTTz/9pFWrVlnW1a5d2/J7wzB09913a/HixXYfEgIAAACUFAV6Qt4dd9yhHTt2aPTo0QoJCZFhGFavSpUqadSoUfr5558VEhLiqpoBAACAQuH0leMsvr6+GjdunMaNG6cTJ04oISFBmZmZqlKliu68805X1AgAAAAUiQKH4+zCwsIUFhbmyl0CAAAARaZAwyoAAACA24lLrhwbhqHffvtNly5dyvOhIC1atHBFlwAAAIDLFSgcJyQkaNSoUVq0aJGuXbuWZ3uTyaSMjIyCdAkAAAAUGqfD8bFjx9SiRQudPXtWhmE4tI2j7QAAAIDi4PSY4xEjRujMmTOqWLGipk6dqmPHjik1NVXp6em5vgAAAICSyukrxz/99JNMJpPmz5+vRx55xJU1AQAAAMXC6XCckpIib29vtWnTxpX1AAAAIB/i4uKsfg4KCmJq3QJwOhzfcccd+uOPP3gkNAAAQLE4LclNMTExVku9vMrp0KE4ArKTnB5z3L17d6WlpWnNmjWurAcAAAAOSZJ0U1KspF/Nr1ilpaUoMTGxWCsrzZwOx6+++qruvfdePf/88zp+/LgrawIAAIDDwiU1Mr/Ci7mW0s/pYRU+Pj766aef9OyzzyoiIkI9evRQkyZNVL58+Vy3i46OdrZLAAAAoFAV6CEg8fHxunz5sq5du6ZZs2Zp1qxZubY3mUyEYwAAAJRYTofjffv2qXXr1kpOTv5zRx4eqlixojw8XPJEagAAAKDIOZ1kx4wZoytXrqhmzZqaOnWqHn74Ybm7u7uyNgAAAKBIOR2ON23aJJPJpAULFqhJkyaurAkAAAAoFk7PVpGSkiIfHx+CMQAAAG4bTofjO++8U+np6crMzHRlPQAAAECxcToc9+7dW9evX9fSpUtdWQ8AAABQbJwOx6+88opat26t559/Xtu2bXNlTQAAAECxcPqGvHfeeUetW7fW7t271bx5c7Vu3VpNmzbN8yEgI0eOdLZLAAAAoFA5HY5HjRolk8kkSTIMQ2vWrNHatWvz3I5wDAAAgJLK6XDcokULSzgGAAAAbgdOh+ONGze6sg4AAACg2Dl9Q15R2759u4YMGaKIiAj5+vqqRo0a6tGjh44cOWLT9uDBg2rfvr3Kly+vwMBA9enTR4mJicVQNQAAAEoTp68cF7V33nlHmzdvVlFzvwYAACAASURBVLdu3VS/fn2dOXNGH374oRo1aqRffvlFd999tyQpPj5erVq1UkBAgCZOnKjk5GRNmjRJ+/bt09atW+XhUWreMgAAAIpYqUmKr776qubPn28Vbrt37657771XEydO1Jw5cyRJEyZMUGpqqnbt2qVq1apJkpo0aaK2bdtq9uzZGjBgQLHUDwAAgJKvwOE4PT1dX3/9tTZu3KhTp07p2rVrMgzDbluTyaQVK1Y41U+zZs1sltWuXVv33HOP4uLiLMsWL16szp07W4KxJLVp00Z169bVwoULCccAAADIUYHC8S+//KLu3bvr1KlTMgzDamo3SVazWWRf70pnz55VRESEJCkhIUHnzp1T48aNbdo1bdpUy5cvd3n/AAAAuH04HY5PnTqlDh06KCkpSffcc4/atWund999V76+vhoyZIjOnDmjNWvW6Pjx4woKCtKAAQPk7u7uytoVGxur+Ph4jR8/XpJ0+vRpSVLVqlVt2latWlUXL15Uenq6ypQp49I6AAAAcHtwOhy/++67Skr6/+3deVhV1f4/8PfezLMgGJPoTUMJNSSnwgFNEYcoJ8xCrUx+eStLb9nVHEoztWva16mybplCZhlmovl1KM3MoUQbFacEBUSPygyH4azfHx72l+M5h+FwDofh/Xqe/Ty69tp7f/ZZLPiwWHvtHERFRWHXrl2QZVlJjt966y0At0eL16xZgxkzZuDMmTNISkoyW+BnzpzB888/j4iICEyaNAkAUFxcDABwcHDQq+/o6KjUYXJMRERERIaYvJTbnj17IEkSFi5cCFk2fBpJkvDCCy9gwYIF2L59O/773/+aHGhV2dnZGDFiBDw9PfHll18q0zWcnJwAAGq1Wu+YkpISnTpERERERHcyeeQ4LS0Nsizrze8tLS3Vq/vCCy/gjTfewIYNGzBlyhRTLwkAyMvLQ3R0NPLy8vDjjz/C19dX2Vc5naJyekVVWVlZ8PLyqtWo8YwZM+Dh4aFTNmHCBEyYMKFesRMRERFR7W3evBmbN2/WKcvNzbXoNU1OjoUQaNWqlc6osYuLC/Ly8vQevmvVqhU8PDx0VpUwhVqtxsiRI3H+/Hns378fnTp10tnv7+8PHx8f/PLLL3rHHj9+HGFhYbW6zsqVKxEeHl6vWImIiIiofgwNTqakpOD++++32DVNnlbh7++PgoICnbKAgABUVFTgzJkzOuVFRUXIyclBYWGhqZeDRqNBbGwsjh07hq1bt6JXr14G640ZMwbJycnIyMhQyvbv34+zZ88iNjbW5OsTERERUfNncnJ89913o7S0FBcvXlTKevfuDQBYv369Tt3/+Z//gRAC7dq1M/VymDlzJnbs2IFhw4ZBpVIhMTFRZ6s0Z84cODs7IzIyEmvWrMGSJUsQGxuL++67D08++aTJ1yciIiKi5s/kaRWRkZHYs2cP9u3bh/j4eADAM888g02bNmHVqlW4cOECunfvjl9//RU7duyAJEl47LHHTA70119/hSRJ2LFjB3bs2KG3/4knngAABAYG4uDBg5g5cyZmz54Ne3t7jBw5EsuXL+cqFURERERULZOT49jYWOzcuRN//fWXUtavXz/MmDEDK1euRHJyMnbu3Km8ECQiIgKzZ882OdDvv/++1nVDQkL4wg8iIiIiqjOTk+O7774bhw4d0it/5513MGTIEHz++ee4fPkyPDw8EB0djaeeeoojt0RERETUqNXr9dHGREdHIzo62hKnJiIiIiKyGJMfyIuPj0d8fDz+/vtvc8ZDRERERGQ1Jo8cf/zxx7C1tcUHH3xgzniIiIiIiKzG5OS4TZs2UKvVOi/7ICIiIiJqykyeVtGzZ0/k5OQgMzPTnPEQEREREVmNycnx9OnTAQBvvPGG2YIhIiIiIrImk5Pjhx56CMuXL8fHH3+Mxx9/HL/99ps54yIiIiIianAmzzkODg4GANjb22PLli3YsmULXF1d4e3tDRsbG4PHSJKE1NRUUy9JRERERGRRJifH58+f1yvLz89Hfn6+0WP48B4RERGZW3p6OlQqlV65t7c3goKCrBARNWUmJ8cffvihOeMgIiIiqrP09HR06hSCkpIivX2Ojs5ITT3NBJnqxOTkeMqUKeaMg4iIiKjOVCqVNjFOABBSZc9plJTEQaVSMTmmOrHI66OJiIiIGlYIgHBrB0HNgMmrVRARERERNTdmHTnOy8tDYWEhhBBG6/j7+5vzkkREREREZlPv5Hj37t1YtWoVDh8+jIKCgmrrSpKE8vLy+l6SiIiIiMgi6pUcz5kzB8uWLat2pLiq2tYjIiIiIrIGk+cc79mzB0uXLoWNjQ2WLFmCX3/9FQDg4+ODM2fO4MCBA5g7dy48PT3h7e2Nb775BufOnTNb4ERERERE5mZycvz+++9DkiTMnTsXr776Krp27QoAsLGxQXBwMPr374+FCxfi5MmTcHNzw9SpU+Hm5ma2wImIiIiIzM3k5Pj48eMAgPj4eJ3yO6dOBAUFYc2aNcjOzsayZctMvRwRERERkcWZnByrVCo4OTnB19dXKbOxsUFxcbFe3aFDh8LBwQHJycmmXo6IiIiIyOJMTo7d3d3h4OCgU+bh4YH8/HwUFem+wlGWZdjb2+PKlSumXo6IiIiIyOJMTo79/f2Rl5cHtVqtlN1zzz0AgJ9++kmn7vnz55Gfnw9bW76Qj4iIiIgaL5OT427dukGj0eDkyZNK2ZAhQyCEwJw5c3Dt2jUAwM2bNxEfHw9JknD//ffXP2IiIiIiIgsxOTmOjo6GEAJff/21Uvbcc8/Bw8MDJ06cQNu2bdGuXTvcddddOHjwIADg5Zdfrn/EREREREQWYnJy/Oijj+LDDz9E7969lbK77roLO3bsQEBAAMrKynD58mVUVFTAyckJq1atwvDhw80SNBERERGRJZg8CdjZ2RlTpkzRK+/bty8uXLiAw4cP4/Lly/Dw8EC/fv3g6elZr0CJiIiIiCzNIk/I2dnZITIy0hKnJiIiIiKyGJOSY41GgzNnziAvLw9eXl4IDg42d1xERERERA2uTnOOy8rK8Oqrr8LLywtdu3ZFREQEQkJC4OPjg8WLF+u9HY+IiIiIqCmp08jxo48+it27d+slwTdu3MD8+fNx7tw5bNiwwZzxERERERE1mFonx19++SW+/fZbAEDHjh0xbtw4BAYG4tKlS0hMTERmZiY2bdqEp556CgMGDLBYwEREREREllLr5DghIQEAEBUVhe3bt+u8Ovq1117DoEGDcPLkSSQmJjI5JiIiIqImqdbJcUpKCiRJwsqVK3USYwBwd3fHsmXLMGTIEJ035hERERFR45Ceng6VSqVT5u3tjaCgICtF1DjVOjlWqVRwdHRESEiIwf09evRQ6hERERFR45Geno5OnUJQUlKkU+7o6IzU1NNMkKuo9WoVarUaHh4eRvdX7lOr1fWPioiIiIjMRqVSaRPjBAAntFsCSkqKOLB5B4u8BISIiIiIGqMQAOHWDqJRq9M6x0REREREzVmdRo6zs7NhY2NjdL8kSdXWkSQJ5eXldYuQiIiIiKiB1Ck55hvwiIiIiKg5q3VyvGDBAkvGQURERERkdUyOiYiIiIi0+EAeEREREZEWk2MiIiIiIi0mx0REREREWkyOiYiIiIi0mBwTEREREWkxOSYiIiIi0mJyTERERESkxeSYiIiIiEiLyTERERERkRaTYyIiIiIiLSbHRERERERaTI6JiIiIiLSYHBMRERERaTE5JiIiIiLSYnJMRERERKRla+0AiIiIiGorPT0dKpVK+f/p06etGA01R0yOiYiIqElIT09Hp04hKCkpsnYo1IxxWgURERE1CSqVSpsYJwA4od0WWTcoanY4ckxERERNTAiAcO2/Oa2CzIsjx0REREREWkyOiYiIiIi0mBwTEREREWkxOSYiIiIi0mJyTERERESk1aSS48LCQixYsADDhg1D69atIcsyNm7caLDumTNnEB0dDTc3N7Ru3RqTJk3SWTSciIiIiOhOTSo5VqlUWLRoEc6cOYOwsDBIkmSwXkZGBvr164eLFy9i6dKleOWVV7Bz505ERUWhvLy8gaMmIiIioqaiSa1z7O/vj6tXr6JNmzY4ceIEevbsabDe4sWLUVxcjFOnTiEgIAAA0LNnTwwZMgQbNmzAM88805BhExEREVET0aRGju3s7NCmTZsa6yUlJWHkyJFKYgwADz30EIKDg/HFF19YMkQiIiIiasKaVHJcG5mZmbh27Rp69Oiht69Xr144efKkFaIiIiIioqag2SXHWVlZAAA/Pz+9fX5+frh58ybKysoaOiwiIiIiagKaXXJcXFwMAHBwcNDb5+joqFOHiIiIiKiqJvVAXm04OTkBANRqtd6+kpISnTpERETUvJ0+fbra/xPdqdklx5XTKSqnV1SVlZUFLy8v2NnZVXuOGTNmwMPDQ6dswoQJmDBhgvkCJSIiIgvKAiAjLi7O2oFQPWzevBmbN2/WKcvNzbXoNZtdcuzv7w8fHx/88ssvevuOHz+OsLCwGs+xcuVKhIeHWyI8IiIiahA5ADQAEgCEVCnfBWCeVSKiujM0OJmSkoL777/fYtdsdnOOAWDMmDFITk5GRkaGUrZ//36cPXsWsbGxVoyMiIiIGlYIgPAq2z+sGw41ek1u5Hjt2rXIyclREt9vvvkGly9fBgBMnz4dbm5umDNnDrZu3YrIyEi8+OKLyM/Px/Lly3HffffhySeftGL0RERERNSYNbnkePny5UhPTwcASJKEbdu2Ydu2bQCAiRMnws3NDYGBgTh48CBmzpyJ2bNnw97eHiNHjsTy5ctrnG9MRERERC1Xk0uO//7771rVCwkJwbfffmvhaIiIiIioOWmWc46JiIiIiEzR5EaOiYiIiKh6XN/ZdEyOiYiIiJoNru9cX0yOiYiIiJoNru9cX0yOiYiIiJqdyvWdK3FaRW3xgTwiIiIiIi0mx0REREREWkyOiYiIiIi0mBwTEREREWkxOSYiIiIi0mJyTERERESkxeSYiIiIiEiLyTERERERkRaTYyIiIiIiLSbHRERERERaTI6JiIiIiLSYHBMRERERadlaOwAiIiJqudLT06FSqfTKvb29ERQUZIWIqKVjckxERERWkZ6ejk6dQlBSUqS3z9HRGampp5kgU4PjtAoiIiKyCpVKpU2MEwCcqLIloKSkyOCIMpGlceSYiIiIrCwEQLi1gyACwJFjIiIiIiIFk2MiIiIiIi0mx0REREREWkyOiYiIiIi0+EAeERERNUqnT5+u9v9ElsDkmIiIiBqZLAAy4uLirB0ItUBMjomIiKiRyQGgwe31j0OqlO8CMM8qEVHLweSYiIiIGqk71z/mtAqyPD6QR0RERESkxeSYiIiIiEiLyTERERERkRaTYyIiIiIiLT6QR0RERNSCGVo/2tvbG0FBQVaIxvqYHBMRERG1SMbXk3Z0dEZq6ukWmSBzWgURERFRi1R1PekTVbYElJQUQaVSWTM4q+HIMREREVGLdud60i0bR46JiIiIiLSYHBMRERERaTE5JiIiIiLSYnJMRERERKTF5JiIiIiISIvJMRERERGRFpNjIiIiIiItJsdERERERFpMjomIiIiItJgcExERERFpMTkmIiIiItJickxEREREpGVr7QBaovT0dKhUKr1yb29vBAUFWSEiIiIi8+HPOWrKmBw3sPT0dHTqFIKSkiK9fY6OzkhNPc1vHERE1GTx5xw1dZxW0cBUKpX2G0YCgBNVtgSUlBQZ/E2biIioqeDPOWrqOHJsNSEAwq0dBBERkYXw5xw1TRw5JiIiIiLSYnJMRERERKTF5JiIiIiISItzjs1Ao9Hg1KlTeuWOjo649957rRAREREREZmCybEZLF68GPPnzze4LykpCaNGjWrgiIiIiBqn06dPG/w3UWPB5NgMLl26BBubrqio2KBTLssRSEtLs05QREREjUoWABlxcXHWDoSoWkyOzUSSXHHnkjWSZGOdYIiIiBqdHAAa3F7/OERbtgvAPKtFRGQIk2MiIiJqQFXXP+a0Cmp8uFoFEREREZEWk2MiIiIiIi0mx0REREREWkyOiYiIiIi0+EBeI2NozUe1Wg0HB4caywDA29sbQUFBFouvMUhPT4dKpdIpM3bfhuoaq1+XukRERNQ8NdvkuLS0FPPmzUNCQgJu3bqFbt264c0338TgwYOtHZoR1a3/aAOgohZlgKOjM1JTTzfbZC49PR2dOoWgpKRIp9zQfRura6h+XeoSERFR89Vsp1VMnjwZ7777LiZOnIhVq1bB1tYWw4cPx08//WTt0Iyouv7jiSrbItxOghNqKDsBIAElJUUGRz+bC5VKpU1gq9674fs2XNdw/brUJSIiouarWSbHx48fx5YtW7B06VIsXboUzzzzDPbv34927dph1qxZ1g6vBpXrP1Zu/zBQbqgsHP+3qHrztHnz5ir/q3rvNd13XT6nlvWZmotu21Bjw/ZpvNg2jRnbpqVqlsnx1q1bYWtri6lTpyplDg4OmDJlCo4cOYKMjAwrRkem4g+Rxott07ixfRovtk1jxrZpqZplcnzq1CkEBwfD1dVVp7xXr17KfiIiIiKiOzXL5DgrKwt+fn565X5+fhBCIDMz0wpREREREVFj1yxXqyguLja4zJmjo6Oy39yEKACQckeZ/moSRERERNR4Ncvk2MnJCWq1Wq+8pKRE2W9IZdJsaK3h6tjb26Oi4ncA9+uUazTAzZs3kZiYqJT9/fff2n/tAlD1OofrUG6s7u1z79q1S+8eZFmGRqOpsayu5Q15jitXrmDXrl3a/1W9d8P3bfyz1q9fl7rmuJfmdo4rV65g8+bNVo/DHOdoijHXdI4rV67ofB9qyvfS3M5RtW2a+r1UqtvPubr87Gvoc1xpJHFY8hzV5xN1zYcaSmVclhjsBACIZmjIkCEiNDRUr3z//v1CkiSRnJxs8LiEhAQBgBs3bty4cePGjVsj3xISEiySRzbLkeOwsDAcOHAABQUFOg/lHT16FJIkISwszOBxQ4cORUJCAtq3b290dJmIiIiIrKe4uBiXLl3C0KFDLXJ+SQghLHJmKzp+/Dj69OmD5cuXY+bMmQBuvzGvS5cu8PHxweHDh2s4AxERERG1RM1y5LhXr14YN24cZs+ejezsbHTs2BEbNmxAWloaPvnkE2uHR0RERESNVLMcOQZujxTPmzcPCQkJuHXrFrp164Y333wTgwcPtnZoRERERNRINdvkmIiIiIiorprlS0CIiIiIiEzRIpLj0tJSvPrqqwgICICzszP69OmDffv21Xjcp59+ClmW9TYbGxtcu3atASJv/goLC7FgwQIMGzYMrVu3hizL2LhxY62Pz83NRXx8PNq0aQNXV1cMGjQIJ0+etGDELUd92oZ9x7J++eUXPP/88+jSpQtcXV3Rrl07jB8/HufOnavV8ew3llOftmG/say//voLsbGx6NChA1xcXODj44MBAwYgOTm5Vsez31hWfdrH3H2nWT6Qd6fJkycjKSkJM2bMUB7OGz58OA4cOIAHH3yw2mMlScKiRYvQvn17nfJWrVpZMOKWQ6VSYdGiRWjXrp2yBF9tCSEwfPhw/P7775g1axZat26NdevWITIyEikpKejQoYPlAm8B6tM2APuOJS1btgw//fQTxo0bh27duuHq1atYvXo1wsPDcezYMdx7771Gj2W/saz6tA3AfmNJaWlpKCgowJNPPgl/f38UFRXhq6++QkxMDNavX49nnnnG6LHsN5ZXn/YBzNx3LLJ6ciNy7NgxIUmSWLFihVJWUlIiOnbsKCIiIqo9dsOGDUKWZXHixAlLh9lilZaWiuzsbCGEEL/88ouQJEl8+umntTp2y5YtQpIkkZSUpJRdv35deHp6iieeeMIi8bYk9Wkb9h3LOnLkiCgrK9MpO3funHB0dBQTJ06s9lj2G8uqT9uw3zQ8jUYjwsLCREhISLX12G+so7btY+6+0+ynVWzduhW2traYOnWqUubg4IApU6bgyJEjyMjIqNV5CgoKDL5Ok+rHzs4Obdq0MenYr776Cr6+vhg1apRS5u3tjdjYWGzfvh1lZWXmCrNFqk/bVMW+Y359+vSBra3uH/46duyI0NDQGl/3yn5jWfVpm6rYbxqGJElo27YtcnJyqq3HfmMdtW2fqszRd5p9cnzq1CkEBwfrvCkPuL0WcuX+6gghEBkZCXd3dzg7O+ORRx7B+fPnLRYv1d7JkycRHh6uV96rVy8UFRXh7NmzVoiKKrHvNLzs7Gx4e3tXW4f9xjpq0zYA+01DKCoqwo0bN3Dx4kWsXLkS3377bY3LvLLfNBxT2gcwb99p9nOOs7Ky4Ofnp1fu5+cHIQQyMzONHuvs7IynnnoKAwcOhLu7O06cOIF33nkHERERSElJQUBAgCVDpxpkZWVhwIABeuWV7Z2ZmYnQ0NCGDovAvmMNCQkJyMjIwJtvvlltPfabhlfbtmG/aRj/+te/8MEHHwAAZFnGmDFjsHr16mqPYb9pOKa0j7n7TrNPjouLi+Hg4KBX7ujoqOw3Zty4cRg3bpzy/5iYGERFRaF///5YvHgx1q1bZ/6Aqdaqa1shRLVtS5bFvtOwzpw5g+effx4RERGYNGlStXXZbxpWXdqG/aZhzJgxA+PGjUNmZia++OILVFRUQK1WV3sM+03DMaV9zN13mv20CicnJ4MfaklJibK/LiIiItC7d+9aLQVHllVd20qSVOe2Jcti37GM7OxsjBgxAp6envjyyy8hSVK19dlvGk5d28YQ9hvzCw4OxqBBgxAXF4dvvvkG+fn5iImJqfYY9puGY0r7GFKfvtPsk2M/Pz9kZWXplVeW+fv71/mcbdu2xc2bN+sdG9WPJdqWLIt9x7zy8vIQHR2NvLw87N69G76+vjUew37TMExpG2PYbyxr7Nix+Pnnn6tdi5r9xnpq0z7GmNp3mn1yHBYWhrNnz6KgoECn/OjRo5AkCWFhYXU+58WLF+Hj42OuEMlEYWFhSElJ0Ss/evQonJ2dERwcbIWoqDrsO+ajVqsxcuRInD9/Hjt37kSnTp1qdRz7jeWZ2jbGsN9YVuWUiNzcXKN12G+spzbtY4ypfafZJ8djx45FeXk51q9fr5SVlpZiw4YN6NOnjzJJ++rVq0hNTUVFRYVST6VS6Z1v165dOHHiBIYNG2b54ElhqH3Gjh2L7OxsJCUlKWUqlQpbt25FTEwM7OzsrBFqi8O+0/A0Gg1iY2Nx7NgxbN26VVl9507sNw2vPm3DfmNZ169f1ysrLy/Hp59+CicnJ+UFLew31lGf9jF335GEEKLORzUx48ePx9dff42XXnpJeUPeL7/8gu+++w4REREAgCeffBIbN27EpUuXEBQUBOD2vJfu3bujR48e8PDwwIkTJ/DJJ58gICAAx48f52/yZrJ27Vrk5OQgIyMD77//PkaPHo3u3bsDAKZPnw43NzeD7aPRaNC3b1/8+eefePnll+Ht7Y1169bh8uXL+Pnnn3HPPfdY87aaBVPbhn3Hsl566SWsWrUKMTExOg+hVHriiScAGP6+xn5jWfVpG/Ybyxo9ejTy8vLQv39/BAQE4OrVq0hMTERqaipWrFiBF198EQD7jbXUp33M3nfM8iqRRk6tVotZs2YJf39/4eTkJHr37i327t2rU+fJJ58UNjY2Ii0tTSmbN2+eCA8PF56ensLBwUG0b99ePP/88+LatWsNfQvNWvv27YUsywa3yvYw1D5CCJGTkyOmTp0qfHx8hKurqxg0aJBISUmxxm00S6a2DfuOZUVGRhptF1mWlXrsNw2vPm3DfmNZW7ZsEVFRUcLPz0/Y29uL1q1bi6ioKJGcnKxTj/3GOurTPubuOy1i5JiIiIiIqDaa/ZxjIiIiIqLaYnJMRERERKTF5JiIiIiISIvJMRERERGRFpNjIiIiIiItJsdERERERFpMjomIiIiItJgcExERERFpMTkmIiIiauSuXr2Kf//73xg0aBDc3d0hyzJ++OGHOp0jMzMTsbGx8PT0hIeHBx599FH8/fffevXUajWWLFmC0NBQuLi4IDAwELGxsfjrr7+qPf/ixYshyzK6detWp7iqOnToEB555BEEBQXByckJfn5+GDZsGH766SeTz1lXTI6JiKjF+O9//wtZlhEcHGztUIjqJDU1Ff/5z3+QmZmJbt26QZKkOh1fWFiIyMhIHDp0CHPnzsXChQtx8uRJREZG4tatWzp1H3/8cbz++usYNGgQVq9ejWeffRY//PADHnzwQVy+fNng+TMyMrBkyRK4urqafI8AcPbsWdjY2GDatGlYt24dXnnlFWRnZ6N///7Ys2dPvc5da/V4DTYRkY7XX39dSJKktzk6OorAwEARExMjvvjiC2uH2SxpNBrx+uuvi9dff11cvnzZ2uE0Wh999JGQJEncc8891g6FSEdkZKR46qmnjO4vKCgQt27dEkIIsXXrViHLsjh48GCtz79s2TIhy7I4ceKEUnbmzBlha2srXnvtNaUsIyNDSJIkXn31VZ3jv//+eyFJknj33XcNnn/8+PFi8ODBIjIyUnTt2rXWcdVGUVGR8PX1FcOGDTPreY3hyDERmZ0kSfD19VU2WZaRmZmJHTt2YPz48RgxYgTKysqsHWazotFo8MYbb2DhwoVIT0+3djhEZGYuLi5o1aqVycd/9dVX6NmzJ8LDw5WyTp064aGHHsIXX3yhlOXn5wMA2rRpo3O8r68vAMDJyUnv3D/88AOSkpLw7rvvVhvDt99+i/79+8PV1RXu7u4YOXJkjVM1Kq/p4+ODnJycGuuaA5NjIrKIzMxMZSssLMQff/yBqKgoAMDu3bsxd+5cK0dIRNQyCCHw22+/oUePHnr7evXqhQsXLqCwsBAA0KFDBwQGBuKdd95BcnIyMjIycPz4cUybNg0dOnTAY489pnO8RqPB9OnTMXXqVISGhhqNXSRFVAAAE6hJREFUYdOmTRg5ciTc3Nzw9ttvY/78+Th9+jT69etn8Bf6/Px83LhxA6mpqZgzZw7+/PNPDB48uJ6fRO0wOSaiBhESEoLt27ejY8eOEELggw8+gEajsXZYzYYQwtohEFEjdfPmTajVavj5+entqyzLzMwEANja2iIpKQnOzs6IiYlB27Zt0adPHxQWFuLw4cNwd3fXOf69995Deno6Fi1aZPT6hYWFePHFFxEfH4+dO3fin//8J15++WUcPXoUGo0Gb731lt4xsbGx8PHxQUhICFasWIH/9//+X4MNqjA5JqIG4+DggHHjxgG4PSpw5swZZV9RURE+++wzTJo0CWFhYWjTpg0cHR0RGBiI0aNHV/sgxp0PWe3fvx+PPPII/P39YWtri/j4eKXuxYsXsXTpUkRHRyM4OBiurq5wc3NDly5dMHPmTFy5csXodfr27QtZlvHWW2+hoqIC77zzDsLCwuDm5oa77roLo0ePxh9//KHULywsxMKFC9GlSxe4uLjAx8cHEyZMwKVLl6r9nEpLS7F27VoMHDgQPj4+cHBwgJ+fH0aNGmXwc4iLi4O9vT0kSYIQQomzcjP08JkQAps2bcKwYcPg6+sLBwcHtGnTBtHR0diyZYvR2AIDAyHLMj777DMUFBRg7ty56Nq1K9zc3JTpMzUJDQ2FLMv497//XW29vXv3QpZl2NnZISsrSynPycnBRx99hNjYWHTt2hVeXl5wcnJC+/btERcXh+PHj9cYgyFxcXGQZVnn6+VOtXmg7/r165gzZw66d++OVq1awcnJCR06dEB8fLzO1zy1XOXl5bhx44ayqVQqlJWVQa1W65TfuHHDLL/4FhcXA7j9PfhOjo6OOnUAoFWrVggLC8OcOXOwfft2vPPOO7h06RLGjh2L0tJSpd7NmzexYMECzJ8/H15eXkavv3fvXuTm5uKxxx7TuTdJktC7d298//33escsW7YMe/fuxccff4wHHngApaWlDTcdr0FmNhNRi1D5QJ4sy0brrFu3Tqlz5MgRpbzyQSlZloWtra3w8vISLi4uQpZlIcuykCRJzJ492+A5qz5ktWLFCuUYLy8v4eDgIKZOnarU7du3r3IdR0dH4e3tLWxtbZVreHp6iqNHjxq8Tt++fYUsy2LBggUiMjJSedjQzc1NOd7Dw0OcOnVKXL9+Xdx3331ClmXh7Oys3IskScLPz09kZGQYvMbFixdFSEiIEqONjY1o1aqVzucwffp0nWOef/554efnpxzj7e0t/Pz8lC0iIkKnvkqlEhEREUp9WZaFp6enzjXGjBkjysvL9eILDAwUsiyLlStXio4dOyqfo6enp7CxsTF6X1UtXbpUSJIk2rZtW229uLg4IcuyGDp0qE753Llzldjt7OyEt7e3cHR0VGK3sbER7733nsFzVvdAXuX1qn691OV4IYTYvXu38PDwUOJzcHDQ+fpwcHAQiYmJ1d43NX8HDhww+PDynZssyyItLU3v+Lo+kKdSqYQkSeLNN9/U27du3Tohy7I4e/asEEKI3Nxc4evrK1asWKFT7+DBg0KSJPH+++8rZc8++6wIDg4WZWVlSpmhB/Lefvvtau/R09Oz2vhLS0tFly5dxLhx42p1v/XFkWMialBVR02rjjR4eXlh1qxZOHz4MAoKCnDjxg0UFBQgIyMDCxYsgK2tLZYtW4bdu3cbPXdGRgZmzZqFZ555BpcvX8aNGzdQXFyM2bNnK3Xuv/9+vP/++zh37hyKi4tx/fp1qNVqHDlyBFFRUcjJyUFsbGy1IxSrVq3CX3/9hW3btqGgoAB5eXk4evQo2rdvj/z8fLz44ouYMmUKCgsLsW/fPhQWFiI/Px//+7//i9atWyM7O9vgnwcLCgowdOhQpKamYvDgwTh06BCKi4tx69Yt3Lp1C8uXL4erqyvWrFmD9957Tzlu9erVOssrffPNNzpzvn/88UdlX0VFBR599FH89NNP6NGjB3bt2oXCwkLcvHkT+fn5+OSTT9CmTRts27YNc+bMMfoZzJ8/HyUlJdi+fbtyfFpaGlq3bm30mEqVI7QZGRn47rvvDNYpKirC119/DQCYNGmSzr7AwEAsXLgQJ06cQFFREa5fv47i4mJcuHABL7zwAjQaDV588UWdUfyGcOrUKYwaNQr5+fmYNm0aTp8+jZKSEuTl5eHSpUt49tlnUVpaiqeffhq//vprg8ZGjUtYWBj27duns3Xr1g1Dhw7VKdu7d6/yIFx9eHl5wcHBQecvMJUqy/z9/QEAW7duxbVr1xATE6NTr3///nB3d8fhw4cBAOfPn8eHH36I6dOnIyMjA2lpabh06RJKSkpQVlaGtLQ0ZYk4jUYDSZKQmJiod9979+5V+roxdnZ2iImJQVJSEtRqdb0/jxo1SApORC1CTSPHubm5IiAgQEiSJLy9vet07srRRkNL+VQddZ4wYYJJsQshREVFhejSpYuQZVls2bJFb3/lqLONjY04fvy43v49e/YooyGurq7i0qVLenXWr18vJEkSbm5uQqPR6OybP3++kCRJDBkyRFRUVBiMcevWrcroc9Xjy8vLlc/g8OHDRu/x448/FpIkiW7duonCwkKDdX7++WchSZJwcnISN2/e1NkXGBgoJEkS9vb24o8//jB6nZoMHjxYyLIsJk+ebHD/pk2blM+pqKioTud+9tlnhSzLYtq0aXr7LDly3L9/fyHLsnj99deNHv/cc88JSZIabASMmo6alnKrypSl3Hr27Cl69+6tVx4VFSU6duyo/H/JkiVClmWRmpqqV9fV1VX5HnvgwAGdvzYZGhGeMWOGEEKIL7/8UkiSJPbu3VvreO/00ksvCVmWxfXr100+R21x5JiILC43Nxf79+/HoEGDkJmZCUmS8NJLL9XpHMOHDwcAZdTCmJrmsVZHlmUMHToUQgid0dY7DRgwAD179tQrHzRoEOzs7CBJEsaPH4927drp1Rk6dCiA2/ORL1y4oLPv448/hiRJmDlzJmTZ8Lfn0aNHw8XFBdnZ2Th16lRdbg/A7TmzkiRh2rRpcHZ2NlinR48e6Ny5M9RqNQ4cOKC3X5IkjBgxoton02syceJECCGQlJSkM9ex0saNGyFJEsaMGWNw6ajqjBgxosY2NLcLFy7g0KFDsLOzw8yZM43WqxwF37t3b0OFRi3Q5cuXkZqaqlM2duxY/Pzzz0hJSVHKUlNT8d133yE2NlYpCw4OhhACn3/+uc7xlX8lqlwKrkuXLti2bRu2bduGr7/+WtlCQ0PRrl07fP3115gyZQqA29/33N3d8dZbb6G8vFwvXpVKpfz7+vXrevtzcnLw1VdfISgoCN7e3iZ8InVja/ErEFGLZCi5kyQJkiRh4sSJBv9kn52djbVr12Lv3r04d+4ccnNzUVFRoVOnchrDnU9MA4Crq2utXlt66NAhfPTRRzh27BgyMjKUJYyqxmnswTxJktCrVy+D+2xsbODl5YVr164ZTJ4B4K677lL+XfWtVOnp6cjIyIAkSZg8ebLR5Bj4vwdn0tLS0L17d6P17lReXo6ff/4ZADB37ly88cYbRuvevHlTuYYhERERtb6uIWPGjMFzzz2HwsJCbNu2DY8//riyLysrS5luMXHiRIPHX7x4EWvXrsXBgwdx4cIF5Ofn661+Ut3DleZW+UubRqOp9mG9yq/nvLw85OTk1GvdWmp53nzzTUiShD///BNCCGzcuBGHDh0CALz22mtKvYkTJ+KHH37Q6RP//Oc/8eGHH2L48OF4+eWXYWtri5UrV8LPz0/nF7qHH34YoaGhWLhwIS5duoQ+ffrg3LlzWLt2LQICAvD0008DAFq3bq039QIAVq5cCUmS8PDDDytlbm5ueO+99zBp0iSEh4fjscceg4+PD9LT07Fz50707dsXq1atAgAMGzYMgYGB6N27N9q0aYO0tDRs2LABWVlZOusxWxKTYyKyiKrz5BwcHODt7Y3u3bvjiSeewIABA/Tq//jjj3j44YeRm5urvBbVzc0NTk5OkCQJFRUVyohCYWGhweS4NiMK//rXv5Rv3sD/JbT29vYAbiffhYWFeglzVW5ubkb32draVluncj8AnXnNVVd5qDqKUp2ioqJa1at63rKyMkiSpPe62Lpe484XBNSVi4sLRo0ahYSEBGzatEknOU5MTIRGo0FgYCAGDRqkd+zWrVsxceJEqNVqpR3d3d3h6OgISZJQWlqKmzdvVtuG5lbZfhUVFbh27Vq1dSt/SSwqKmJyTHUyf/585WtekiR88sknyr+rJseSJOn9gu3q6oqDBw9ixowZWLx4MTQaDQYOHIgVK1boPCtgZ2eHH3/8EYsWLcLOnTvx+eefw83NDaNHj8bixYurXZWi6vXvNGHCBAQEBGDp0qVYvnw51Go1AgIC0K9fPzz11FNKvSlTpuDzzz/Hu+++i5ycHHh6euKBBx7AK6+8ggcffLBuH5iJmBwTkUXUZkmvSmVlZXj88ceRl5eHHj164K233sKDDz6o82f/s2fPonPnzgCMr+lrY2NT7XV2796tJMbTp09HfHw8OnfurPONfM6cOVi6dGmDrxtcdYT8/Pnz+Mc//mHRa+zbtw8DBw40+Vw1fda1MWnSJCQkJGD//v3Izs5WRtUTEhIgSRLi4uL0jlGpVHj66adRWlqKqKgozJ8/Hz169FB+uQGAPXv2IDo6ukHbsPKzDQgI4BsKySSGljO7U23Xhjd2Ln9//2qXaqzk4eGB5cuXY/ny5bW6Xm2uDdx+qK9///7VHj9t2jRMmzatztc1J845JiKrO3z4MK5cuQJbW1skJydj8ODBevNhr169Wu/rVM6hGzFiBFauXImQkBC9EQ5zXMcUVUfaa1oH2VQ+Pj7KaJKlrlEXDz30EPz9/VFRUYHExEQAwO+//47ffvsNgOEpFcnJySgoKIC3tze2b9+OBx98UCcxBkxvQ1tbWwghUFJSYrRObm6uwfLK9rt27ZrOOrBE1PQwOSYiq6tchszX19fon+v37dtnlutIkmR0nq4QAt9//73BPwlaWocOHZSR0x07dtT5+Kp/QjU2Ympvb6+8PtaUa5ibJEl44oknlBeSALcfxAOA8PBwhISE6B1T+bXSuXNngy80AEz/WvH09NS5hiHHjh0zWF45B7usrKza5QaJqPFjckxEVufh4QHg9lSMygfBqkpPT8fatWvNch0hhNE1ZtesWWP0AbSGMHXqVAghsH79evz+++/V1r1zzrAkSXBxcQFw+8luY+Lj4yGEwI4dO2pcMaG285Lro3L1ht9++w2nTp3C5s2blYcSDan8WklNTTW4FnVKSkqt/mxsyH333QcAOHr0qMHR5z///BPbt283+MtT586d0bdvXwghMHv2bBQUFFR7rYb4bInINEyOicjq+vXrB2dnZ2g0GowdOxbnz58HcHt+3bfffouBAweaZY5rdHQ0gNujpkuWLFEeNsvJycGiRYswY8aMBlkmyJhZs2bh3nvvRVFREQYMGID33ntPJ4nKzc3Frl27EBcXh8jISL3ju3TpAiEEEhISjE4NmDx5MgYOHIiKigrExMRgyZIlOolgUVERDhw4gGnTpuGee+4x+z3eKTQ0FGFhYQBuJ+6ZmZmwtbXFY489ZrB+VFQUJEnC9evXERcXp7zAoLS0FJ9//jmGDh2qJNB1FRMTA2dnZ5SWlmLcuHHK12F5eTm2bduGqKgouLm5GR2ZX7t2LVxcXHD69Gn06dMHycnJOi8syMjIwMaNG/HQQw8ZfAkMETUSFl9JmYhajNq8PtqYNWvW6Cwo7+7uLhwdHYUkScLX11fs2LFDOfedryiu6ZW+lUpLS5VXQFeey8vLS9jY2AhZlsWoUaPE7NmzlRdx3Kny2MWLFxu9RuXrlY29Iriml3VkZGSI3r1768To6ekp3N3ddRbXDw0N1Tt2w4YNynH29vaibdu2on379iIyMlKnXm5urhgxYoTO4v0eHh7Ka6ory1xcXOp8f6ZYuXKlzssDHn744Wrrv/LKKzpxtmrVStjb2wtJkkRwcLBITEwUkiQJOzs7vWNr+lpZv3693tehg4ODkCRJ9O/fX6xevbra43/88Ufh5+enHG9rayu8vb2Fs7OzTvs999xzdf+giKhBcOSYiMyqcpmqunruueewY8cODBgwAG5ubigvL0dQUBBmzJiBX3/9VZl/auzctbmunZ0d9u3bh3nz5iE4OBj29vaQJAkPPPAA1q9fj6SkJNjY2Jh8D1VjMbWOv78/jhw5gsTERMTExMDPzw9FRUUoLy/H3XffjUceeQSrV682+NrlyZMn49NPP0Xfvn3h4uKCzMxMpKen660c4u7ujuTkZCQnJ2P8+PEICgpCaWkp1Go1AgMDER0djWXLluHPP/80+f7q4vHHH1deniLLstEpFZXefvttbNiwAb169YKzszMqKioQHByMefPmISUlBXfddVe1bVjdvqlTpyI5ORmRkZFwd3dHRUUFOnfujP/85z/47rvvlKUFjR0fERGBs2fP4u2330b//v3h6emJ3Nxc2NraIjQ0FJMmTcJnn31m0ioARNQwJCEaeL0iIiIiIqJGiiPHRERERERaTI6JiIiIiLSYHBMRERERaTE5JiIiIiLSYnJMRERERKTF5JiIiIiISIvJMRERERGRFpNjIiIiIiItJsdERERERFpMjomIiIiItJgcExERERFpMTkmIiIiItJickxEREREpMXkmIiIiIhI6/8DwfQjZKBCCl8AAAAASUVORK5CYII=",
      "text/plain": [
       "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x3274fb450>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "PyObject <matplotlib.text.Text object at 0x33299e310>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# bootstrap performance\n",
    "param_interest_boot_stat =2\n",
    "peak_interest_boot_stat = 3\n",
    "\n",
    "# Graphics\n",
    "fig = figure()\n",
    "plot(1:nb_boot, bootrecord[:,param_interest_boot_stat,peak_interest_boot_stat],label=\"SOI\")\n",
    "\n",
    "title(\"Figure 4: Standard deviation\",fontsize = 18, fontweight = \"bold\")\n",
    "\n",
    "xlabel(\"Number of iterations during bootstrap\",fontsize=18)\n",
    "ylabel(\"Parameter $(param_interest_boot_stat), peak $(peak_interest_boot_stat)\",fontsize=18)\n",
    "\n",
    "fi2= figure()\n",
    "h = plt[:hist](params_boot[:,param_interest_boot_stat,peak_interest_boot_stat],100)\n",
    "\n",
    "title(\"Figure 5: Average value\",fontsize = 18, fontweight = \"bold\")\n",
    "\n",
    "xlabel(\"Parameter value\",fontsize=18)\n",
    "ylabel(\"Parameter $(param_interest_boot_stat), peak $(peak_interest_boot_stat)\",fontsize=18)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Julia 0.5.0",
   "language": "julia",
   "name": "julia-0.5"
  },
  "language_info": {
   "file_extension": ".jl",
   "mimetype": "application/julia",
   "name": "julia",
   "version": "0.5.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
